
Perform ANOVA to test the significance at 1% level of significance.

Answer to Problem 37E
The ANOVA for the given data is shown below:
Source |
Degrees of freedom |
Sum of squares |
Mean sum of squares | F-ratio |
Fabric A | 2 | 4,414.658 | 2207.329 | 2259.293 |
Type of exposure B | 1 | 47.255 | 47.255 | 48.36745 |
Degree of exposure C | 2 | 983.566 | 491.783 | 503.3603 |
Fabric direction D | 1 | 0.044 | 0.044 | 0.045036 |
Interaction AB | 2 | 30.606 | 15.303 | 15.66325 |
Interaction AC | 2 | 1,101.754 | 275.446 | 281.9304 |
Interaction AD | 2 | 0.94 | 0.47 | 0.481064 |
Interaction BC | 2 | 4.282 | 2.141 | 2.191402 |
Interaction BD | 1 | 0.273 | 0.273 | 0.279427 |
Interaction CD | 2 | 0.494 | 0.247 | 0.252815 |
Interaction ABC | 4 | 14.856 | 3.714 | 3.801433 |
Interaction ABD | 2 | 8.144 | 4.072 | 4.167861 |
Interaction ACD | 4 | 3.068 | 0.767 | 0.785056 |
Interaction BCD | 2 | 0.56 | 0.28 | 0.286592 |
Interaction ABCD | 4 | 1.389 | 0.347 | 0.355 |
Error | 36 | 35.172 | 0.977 | |
Total | 71 | 6,647.091 | 9.621 |
There is sufficient of evidence to conclude that there is an effect of fabric on the extent of color change at 1% level of significance.
There is sufficient of evidence to conclude that there is an effect exposure type on the extent of color change at 1% level of significance.
There is sufficient of evidence to conclude that there is an effect of exposure level on the extent of color change at 1% level of significance.
There is no sufficient of evidence to conclude that there is an effect of fabric direction on the extent of color change at 1% level of significance.
There is sufficient of evidence to conclude that there is an interaction effect of fabric and exposure type on the extent of color change at 1% level of significance.
There is sufficient of evidence to conclude that there is an interaction effect of fabric and exposure level on the extent of color change at 1% level of significance.
There is no sufficient of evidence to conclude that there is an interaction effect of fabric and fabric direction on the extent of color change at 1% level of significance.
There is no sufficient of evidence to conclude that there is an interaction effect of exposure type and exposure level on the extent of color change at 1% level of significance.
There is no sufficient of evidence to conclude that there is an interaction effect of exposure type and fabric direction on the extent of color change at 1% level of significance.
There is no sufficient of evidence to conclude that there is an interaction effect of exposure level and fabric direction on the extent of color change at 1% level of significance.
There is no sufficient of evidence to conclude that there is an interaction effect of fabric, exposure type and exposure level on the extent of color change at 1% level of significance.
There is no sufficient of evidence to conclude that there is an interaction effect of fabric, exposure type and fabric direction on the extent of color change at 1% level of significance.
There is no sufficient of evidence to conclude that there is an interaction effect of fabric, exposure level and fabric direction on the extent of color change at 1% level of significance.
There is no sufficient of evidence to conclude that there is an interaction effect of exposure type, exposure level and fabric direction on the extent of color change at 1% level of significance.
There is no sufficient of evidence to conclude that there is an interaction effect of fabric, exposure type, exposure level and fabric direction on the extent of color change at 1% level of significance.
Explanation of Solution
Given info:
An experiment was conducted to test the effect of fabric, type of exposure, level of exposure and fabric direction on the color change of the fabric. Two observation were noted for each of the four factors.
Calculation:
The general ANOVA table is given below:
Source | Degrees of freedom | Sum of squares | Mean sum of squares | F-ratio |
Factor A | ||||
Factor B | ||||
Factor C | ||||
Factor D | ||||
Interaction AB | ||||
Interaction ABC | ||||
Error | ||||
Total |
The sum of squares for each factor and interaction is calculated by multiplying the mean sum of squares with its corresponding degrees of freedom.
Sum of squares excluding ABCD:
Source | Sum of squares |
A | 4,414.658 |
B | 47.255 |
C | 983.566 |
D | 0.044 |
AB | 30.606 |
AC | 1,101.784 |
AD | 0.94 |
BC | 4.282 |
BD | 0.273 |
CD | 0.494 |
ABC | 14.856 |
ABD | 8.144 |
ACD | 3.068 |
BCD | 0.56 |
Error | 35.172 |
Total | 6,647.091 |
Using the above table SSABCD can be calculated:
The mean sum of squares for the interaction ABCD is given below:
Thus, the mean sum of squares for the interaction ABCD is 0.347.
The ANOVA for the given data is shown below:
Source | Degrees of freedom |
Sum of squares |
Mean sum of squares | F-ratio |
Fabric A | 4,414.658 | 2207.329 | 2,259.293 | |
Type of exposure B | 47.255 | 47.255 | 48.36745 | |
Degree of exposure C | 983.566 | 491.783 | 503.3603 | |
Fabric direction D | 0.044 | 0.044 | 0.045036 | |
Interaction AB | 30.606 | 15.303 | 15.66325 | |
Interaction AC | 1,101.754 | 275.446 | 281.9304 | |
Interaction AD | 0.94 | 0.47 | 0.481064 | |
Interaction BC | 4.282 | 2.141 | 2.191402 | |
Interaction BD | 0.273 | 0.273 | 0.279427 | |
Interaction CD | 0.494 | 0.247 | 0.252815 | |
Interaction ABC | 14.856 | 3.714 | 3.801433 | |
Interaction ABD | 8.144 | 4.072 | 4.167861 | |
Interaction ACD | 3.068 | 0.767 | 0.785056 | |
Interaction BCD | 0.56 | 0.28 | 0.286592 | |
Interaction ABCD | 1.389 | 0.347 | 0.355 | |
Error | 35.172 | 0.977 | ||
Total | 6,647.091 | 9.621 |
Where,
The F statistic for each factor is obtained by dividing the mean sum of squares with the mean sum of squares due to error.
Testing the main effects:
Testing the Hypothesis for the factor A:
Null hypothesis:
That is, there is no significant difference in the extent of color change due to the three levels of fabrics.
Alternative hypothesis:
That is, there is significant difference in the extent of color change due to the three levels of fabrics.
Testing the Hypothesis for the factor B:
Null hypothesis:
That is, there is no significant difference in the extent of color change due to the two levels of exposure type.
Alternative hypothesis:
That is, there is a significant difference in the extent of color change due to the two levels of exposure type.
Testing the Hypothesis for the factor C:
Null hypothesis:
That is, there is no significant difference in the extent of color change due to the three levels of exposure level.
Alternative hypothesis:
That is, there is a significant difference in the extent of color change due to the three levels of exposure level.
Testing the Hypothesis for the factor D:
Null hypothesis:
That is, there is no significant difference in the extent of color change due to the two levels of fabric direction.
Alternative hypothesis:
That is, there is a significant difference in the extent of color change due to the two levels of fabric direction.
Testing the Hypothesis for the interaction effect of AB:
Null hypothesis:
That is, there is no significant difference in the extent of color change due to the interaction between fabric and exposure type.
Alternative hypothesis:
That is, there is significant difference in the extent of color change due to the interaction between fabric and exposure type.
Testing the Hypothesis for the interaction effect AC:
Null hypothesis:
That is, there is no significant difference in the extent of color change due to the interaction between fabric and exposure level.
Alternative hypothesis:
That is, there is a significant difference in the extent of color change due to the interaction between fabric and exposure level.
Testing the Hypothesis for the interaction effect AD:
Null hypothesis:
That is, there is no significant difference in the extent of color change due to the interaction between fabric and fabric direction.
Alternative hypothesis:
That is, there is a significant difference in the extent of color change due to the interaction between fabric and fabric direction.
Testing the Hypothesis for the interaction effect BC:
Null hypothesis:
That is, there is no significant difference in the extent of color change due to the interaction between exposure type and exposure level.
Alternative hypothesis:
That is, there is significant difference in the extent of color change due to the interaction between exposure type and exposure level.
Testing the Hypothesis for the interaction effect BD:
Null hypothesis:
That is, there is no significant difference in the extent of color change due to the interaction between exposure type and fabric direction.
Alternative hypothesis:
That is, there is significant difference in the extent of color change due to the interaction between exposure type and fabric direction.
Testing the Hypothesis for the interaction effect CD:
Null hypothesis:
That is, there is no significant difference in the extent of color change due to the interaction between exposure level and fabric direction.
Alternative hypothesis:
That is, there is significant difference in the extent of color change due to the interaction between exposure level and fabric direction.
Testing the Hypothesis for the interaction effect ABC:
Null hypothesis:
That is, there is no significant difference in the extent of color change due to the interaction between fabric, exposure type and exposure level.
Alternative hypothesis:
That is, there is a significant difference in the extent of color change due to the interaction between fabric, exposure type and exposure level.
Testing the Hypothesis for the interaction effect ABD:
Null hypothesis:
That is, there is no significant difference in the extent of color change due to the interaction between fabric, exposure type and fabric direction.
Alternative hypothesis:
That is, there is a significant difference in the extent of color change due to the interaction between fabric, exposure type and fabric direction.
Testing the Hypothesis for the interaction effect ACD:
Null hypothesis:
That is, there is no significant difference in the extent of color change due to the interaction between fabric, exposure level and fabric direction.
Alternative hypothesis:
That is, there is a significant difference in the extent of color change due to the interaction between fabric, exposure level and fabric direction.
Testing the Hypothesis for the interaction effect BCD:
Null hypothesis:
That is, there is no significant difference in the extent of color change due to the interaction between exposure type, exposure level and fabric direction.
Alternative hypothesis:
That is, there is a significant difference in the extent of color change due to the interaction between exposure type, exposure level and fabric direction.
Testing the Hypothesis for the interaction effect ABCD:
Null hypothesis:
That is, there is no significant difference in the extent of color change due to the interaction between exposure type, exposure level and fabric direction.
Alternative hypothesis:
That is, there is a significant difference in the extent of color change due to the interaction between fabric, exposure type, exposure level and fabric direction.
P-value for the main effect of A:
Software procedure:
Step-by-step procedure to find the P-value is given below:
- Click on Graph, select View Probability and click OK.
- Select F, enter 2 in numerator df and 36 in denominator df.
- Under Shaded Area Tab select X value under Define Shaded Area By and select right tails.
- Choose X value as 2,259.29.
- Click OK.
Output obtained from MINITAB is given below:
P-value for the main effect of B:
Software procedure:
Step-by-step procedure to find the P-value is given below:
- Click on Graph, select View Probability and click OK.
- Select F, enter 1 in numerator df and 36 in denominator df.
- Under Shaded Area Tab select X value under Define Shaded Area By and select right tails.
- Choose X value as 48.37.
- Click OK.
Output obtained from MINITAB is given below:
P-value for the main effect of C:
Software procedure:
Step-by-step procedure to find the P-value is given below:
- Click on Graph, select View Probability and click OK.
- Select F, enter 2 in numerator df and 36 in denominator df.
- Under Shaded Area Tab select X value under Define Shaded Area By and select right tails.
- Choose X value as 503.36.
- Click OK.
Output obtained from MINITAB is given below:
P-value for the main effect of D:
Software procedure:
Step-by-step procedure to find the P-value is given below:
- Click on Graph, select View Probability and click OK.
- Select F, enter 1 in numerator df and 36 in denominator df.
- Under Shaded Area Tab select X value under Define Shaded Area By and select right tails.
- Choose X value as 0.05.
- Click OK.
Output obtained from MINITAB is given below:
P-value for the interaction effect of A and B:
Software procedure:
Step-by-step procedure to find the P-value is given below:
- Click on Graph, select View Probability and click OK.
- Select F, enter 2 in numerator df and 36 in denominator df.
- Under Shaded Area Tab select X value under Define Shaded Area By and select right tails.
- Choose X value as 15.66.
- Click OK.
Output obtained from MINITAB is given below:
P-value for the interaction effect of A and C:
Software procedure:
Step-by-step procedure to find the P-value is given below:
- Click on Graph, select View Probability and click OK.
- Select F, enter 4 in numerator df and 36 in denominator df.
- Under Shaded Area Tab select X value under Define Shaded Area By and select right tails.
- Choose X value as 281.93.
- Click OK.
Output obtained from MINITAB is given below:
P-value for the interaction effect of A and D:
Software procedure:
Step-by-step procedure to find the P-value is given below:
- Click on Graph, select View Probability and click OK.
- Select F, enter 2 in numerator df and 36 in denominator df.
- Under Shaded Area Tab select X value under Define Shaded Area By and select right tails.
- Choose X value as 0.48.
- Click OK.
Output obtained from MINITAB is given below:
P-value for the interaction effect of B and C:
Software procedure:
Step-by-step procedure to find the P-value is given below:
- Click on Graph, select View Probability and click OK.
- Select F, enter 2 in numerator df and 36 in denominator df.
- Under Shaded Area Tab select X value under Define Shaded Area By and select right tails.
- Choose X value as 2.19.
- Click OK.
Output obtained from MINITAB is given below:
P-value for the interaction effect of B and D:
Software procedure:
Step-by-step procedure to find the P-value is given below:
- Click on Graph, select View Probability and click OK.
- Select F, enter 1 in numerator df and 36 in denominator df.
- Under Shaded Area Tab select X value under Define Shaded Area By and select right tails.
- Choose X value as 0.28.
- Click OK.
Output obtained from MINITAB is given below:
P-value for the interaction effect of C and D:
Software procedure:
Step-by-step procedure to find the P-value is given below:
- Click on Graph, select View Probability and click OK.
- Select F, enter 2 in numerator df and 36 in denominator df.
- Under Shaded Area Tab select X value under Define Shaded Area By and select right tails.
- Choose X value as 0.25.
- Click OK.
Output obtained from MINITAB is given below:
P-value for the interaction effect of A, B and C:
Software procedure:
Step-by-step procedure to find the P-value is given below:
- Click on Graph, select View Probability and click OK.
- Select F, enter 4 in numerator df and 36 in denominator df.
- Under Shaded Area Tab select X value under Define Shaded Area By and select right tails.
- Choose X value as 3.80.
- Click OK.
Output obtained from MINITAB is given below:
P-value for the interaction effect of A, B and D:
Software procedure:
Step-by-step procedure to find the P-value is given below:
- Click on Graph, select View Probability and click OK.
- Select F, enter 2 in numerator df and 36 in denominator df.
- Under Shaded Area Tab select X value under Define Shaded Area By and select right tails.
- Choose X value as 4.17.
- Click OK.
Output obtained from MINITAB is given below:
P-value for the interaction effect of A, C and D:
Software procedure:
Step-by-step procedure to find the P-value is given below:
- Click on Graph, select View Probability and click OK.
- Select F, enter 4 in numerator df and 36 in denominator df.
- Under Shaded Area Tab select X value under Define Shaded Area By and select right tails.
- Choose X value as 0.79.
- Click OK.
Output obtained from MINITAB is given below:
P-value for the interaction effect of B, C and D:
Software procedure:
Step-by-step procedure to find the P-value is given below:
- Click on Graph, select View Probability and click OK.
- Select F, enter 2 in numerator df and 36 in denominator df.
- Under Shaded Area Tab select X value under Define Shaded Area By and select right tails.
- Choose X value as 0.29.
- Click OK.
Output obtained from MINITAB is given below:
P-value for the interaction effect of A, B, C and D:
Software procedure:
Step-by-step procedure to find the P-value is given below:
- Click on Graph, select View Probability and click OK.
- Select F, enter 4 in numerator df and 36 in denominator df.
- Under Shaded Area Tab select X value under Define Shaded Area By and select right tails.
- Choose X value as 0.355.
- Click OK.
Output obtained from MINITAB is given below:
Conclusion:
For the main effect of A:
The P- value for the factor A (fabric) is 0.000 and the level of significance is 0.01.
Here, the P- value is lesser than the level of significance.
That is,
Thus, the null hypothesis is rejected,
Hence, there is sufficient of evidence to conclude that there is an effect of fabric on the extent of color change at 1% level of significance.
For main effect of B:
The P- value for the factor B (exposure level) is 0.000 and the level of significance is 0.01.
Here, the P- value is lesser than the level of significance.
That is,
Thus, the null hypothesis is not rejected.
Hence, there is sufficient of evidence to conclude that there is an effect exposure type on the extent of color change at 1% level of significance.
For main effect of C:
The P- value for the factor C (exposure level) is 0.000 and the level of significance is 0.01.
Here, the P- value is lesser than the level of significance.
That is,
Thus, the null hypothesis is rejected.
Hence, there is sufficient of evidence to conclude that there is an effect of exposure level on the extent of color change at 1% level of significance.
For main effect of D:
The P- value for the factor D (fabric direction) is 0.8243 and the level of significance is 0.01.
Here, the P- value is greater than the level of significance.
That is,
Thus, the null hypothesis is not rejected.
Hence, there is no sufficient of evidence to conclude that there is an effect of fabric direction on the extent of color change at 1% level of significance.
Interaction effect of factor A and B:
The P- value for the interaction effect AB (fabric and exposure type) is 0.000 and the level of significance is 0.01.
Here, the P- value is lesser than the level of significance.
That is,
Thus, the null hypothesis is rejected,
Hence, there is sufficient of evidence to conclude that there is an interaction effect of fabric and exposure type on the extent of color change at 1% level of significance.
Interaction effect of factor A and C:
The P- value for the interaction effect AC (fabric and exposure level) is 0.000 and the level of significance is 0.01.
Here, the P- value is lesser than the level of significance.
That is,
Thus, the null hypothesis is rejected.
Hence, there is sufficient of evidence to conclude that there is an interaction effect of fabric and exposure level on the extent of color change at 1% level of significance.
Interaction effect of factor A and D:
The P- value for the interaction effect AD (fabric and fabric direction) is 0.6227 and the level of significance is 0.01.
Here, the P- value is greater than the level of significance.
That is,
Thus, the null hypothesis is not rejected.
Hence, there is no sufficient of evidence to conclude that there is an interaction effect of fabric and fabric direction on the extent of color change at 1% level of significance.
Interaction effect of factor B and C:
The P- value for the interaction effect BC (exposure type and exposure level) is 0.1266 and the level of significance is 0.01.
Here, the P- value is greater than the level of significance.
That is,
Thus, the null hypothesis is not rejected,
Hence, there is no sufficient of evidence to conclude that there is an interaction effect of exposure type and exposure level on the extent of color change at 1% level of significance.
Interaction effect of factor B and D:
The P- value for the interaction effect BD (exposure type and fabric direction) is 0.5999 and the level of significance is 0.01.
Here, the P- value is greater than the level of significance.
That is,
Thus, the null hypothesis is not rejected,
Hence, there is no sufficient of evidence to conclude that there is an interaction effect of exposure type and fabric direction on the extent of color change at 1% level of significance.
Interaction effect of factor C and D:
The P- value for the interaction effect CD (exposure level and fabric direction) is 0.7801 and the level of significance is 0.01.
Here, the P- value is greater than the level of significance.
That is,
Thus, the null hypothesis is not rejected,
Hence, there is no sufficient of evidence to conclude that there is an interaction effect of exposure level and fabric direction on the extent of color change at 1% level of significance.
Interaction effect of factor A,B and C:
The P- value for the interaction effect ABC (fabric, exposure type and exposure level) is 0.01119 and the level of significance is 0.01.
Here, the P- value is greater than the level of significance.
That is,
Thus, the null hypothesis is not rejected.
Hence, there is no sufficient of evidence to conclude that there is an interaction effect of fabric, exposure type and exposure level on the extent of color change at 1% level of significance.
Interaction effect of factor A,B and D:
The P- value for the interaction effect ABD (fabric, exposure type and fabric direction) is 0.0235 and the level of significance is 0.01.
Here, the P- value is greater than the level of significance.
That is,
Thus, the null hypothesis is not rejected.
Hence, there is no sufficient of evidence to conclude that there is an interaction effect of fabric, exposure type and fabric direction on the extent of color change at 1% level of significance.
Interaction effect of factor A,C and D:
The P- value for the interaction effect ACD (fabric, exposure level and fabric direction) is 0.5394 and the level of significance is 0.01.
Here, the P- value is greater than the level of significance.
That is,
Thus, the null hypothesis is not rejected.
Hence, there is no sufficient of evidence to conclude that there is an interaction effect of fabric, exposure level and fabric direction on the extent of color change at 1% level of significance.
Interaction effect of factor B, C and D:
The P- value for the interaction effect BCD (exposure type, exposure level and fabric direction) is 0.7500 and the level of significance is 0.01.
Here, the P- value is greater than the level of significance.
That is,
Thus, the null hypothesis is not rejected.
Hence, there is no sufficient of evidence to conclude that there is an interaction effect of exposure type, exposure level and fabric direction on the extent of color change at 1% level of significance.
Interaction effect of factor A, B, C and D:
The P- value for the interaction effect ABCD (fabric, exposure type, exposure level and fabric direction) is 0.8388 and the level of significance is 0.01.
Here, the P- value is greater than the level of significance.
That is,
Thus, the null hypothesis is not rejected.
Hence, there is no sufficient of evidence to conclude that there is an interaction effect of fabric, exposure type, exposure level and fabric direction on the extent of color change at 1% level of significance.
Therefore, there is significant difference in the extent of color change with respect to the main effect A, B, D and interaction effects AB, AC are significant at 1% level of significance. The remaining second order interactions and third order interaction are not significant at 1% level of significance.
Want to see more full solutions like this?
Chapter 11 Solutions
Bundle: Probability and Statistics for Engineering and the Sciences, 9th + WebAssign Printed Access Card for Devore's Probability and Statistics for ... and the Sciences, 9th Edition, Single-Term
- According to an economist from a financial company, the average expenditures on "furniture and household appliances" have been lower for households in the Montreal area than those in the Quebec region. A random sample of 14 households from the Montreal region and 16 households from the Quebec region was taken, providing the following data regarding expenditures in this economic sector. It is assumed that the data from each population are distributed normally. We are interested in knowing if the variances of the populations are equal. a) Perform the appropriate hypothesis test on two variances at a significance level of 1%. Include the following information: i. Hypothesis / Identification of populations ii. Critical F-value(s) iii. Decision rule iv. F-ratio value v. Decision and conclusion b) Based on the results obtained in a), is the hypothesis of equal variances for this socio-economic characteristic measured in these two populations upheld? c) Based on the results obtained in a),…arrow_forwardA major company in the Montreal area, offering a range of engineering services from project preparation to construction execution, and industrial project management, wants to ensure that the individuals who are responsible for project cost estimation and bid preparation demonstrate a certain uniformity in their estimates. The head of civil engineering and municipal services decided to structure an experimental plan to detect if there could be significant differences in project evaluation. Seven projects were selected, each of which had to be evaluated by each of the two estimators, with the order of the projects submitted being random. The obtained estimates are presented in the table below. a) Complete the table above by calculating: i. The differences (A-B) ii. The sum of the differences iii. The mean of the differences iv. The standard deviation of the differences b) What is the value of the t-statistic? c) What is the critical t-value for this test at a significance level of 1%?…arrow_forwardCompute the relative risk of falling for the two groups (did not stop walking vs. did stop). State/interpret your result verbally.arrow_forward
- Microsoft Excel include formulasarrow_forwardQuestion 1 The data shown in Table 1 are and R values for 24 samples of size n = 5 taken from a process producing bearings. The measurements are made on the inside diameter of the bearing, with only the last three decimals recorded (i.e., 34.5 should be 0.50345). Table 1: Bearing Diameter Data Sample Number I R Sample Number I R 1 34.5 3 13 35.4 8 2 34.2 4 14 34.0 6 3 31.6 4 15 37.1 5 4 31.5 4 16 34.9 7 5 35.0 5 17 33.5 4 6 34.1 6 18 31.7 3 7 32.6 4 19 34.0 8 8 33.8 3 20 35.1 9 34.8 7 21 33.7 2 10 33.6 8 22 32.8 1 11 31.9 3 23 33.5 3 12 38.6 9 24 34.2 2 (a) Set up and R charts on this process. Does the process seem to be in statistical control? If necessary, revise the trial control limits. [15 pts] (b) If specifications on this diameter are 0.5030±0.0010, find the percentage of nonconforming bearings pro- duced by this process. Assume that diameter is normally distributed. [10 pts] 1arrow_forward4. (5 pts) Conduct a chi-square contingency test (test of independence) to assess whether there is an association between the behavior of the elderly person (did not stop to talk, did stop to talk) and their likelihood of falling. Below, please state your null and alternative hypotheses, calculate your expected values and write them in the table, compute the test statistic, test the null by comparing your test statistic to the critical value in Table A (p. 713-714) of your textbook and/or estimating the P-value, and provide your conclusions in written form. Make sure to show your work. Did not stop walking to talk Stopped walking to talk Suffered a fall 12 11 Totals 23 Did not suffer a fall | 2 Totals 35 37 14 46 60 Tarrow_forward
- Question 2 Parts manufactured by an injection molding process are subjected to a compressive strength test. Twenty samples of five parts each are collected, and the compressive strengths (in psi) are shown in Table 2. Table 2: Strength Data for Question 2 Sample Number x1 x2 23 x4 x5 R 1 83.0 2 88.6 78.3 78.8 3 85.7 75.8 84.3 81.2 78.7 75.7 77.0 71.0 84.2 81.0 79.1 7.3 80.2 17.6 75.2 80.4 10.4 4 80.8 74.4 82.5 74.1 75.7 77.5 8.4 5 83.4 78.4 82.6 78.2 78.9 80.3 5.2 File Preview 6 75.3 79.9 87.3 89.7 81.8 82.8 14.5 7 74.5 78.0 80.8 73.4 79.7 77.3 7.4 8 79.2 84.4 81.5 86.0 74.5 81.1 11.4 9 80.5 86.2 76.2 64.1 80.2 81.4 9.9 10 75.7 75.2 71.1 82.1 74.3 75.7 10.9 11 80.0 81.5 78.4 73.8 78.1 78.4 7.7 12 80.6 81.8 79.3 73.8 81.7 79.4 8.0 13 82.7 81.3 79.1 82.0 79.5 80.9 3.6 14 79.2 74.9 78.6 77.7 75.3 77.1 4.3 15 85.5 82.1 82.8 73.4 71.7 79.1 13.8 16 78.8 79.6 80.2 79.1 80.8 79.7 2.0 17 82.1 78.2 18 84.5 76.9 75.5 83.5 81.2 19 79.0 77.8 20 84.5 73.1 78.2 82.1 79.2 81.1 7.6 81.2 84.4 81.6 80.8…arrow_forwardName: Lab Time: Quiz 7 & 8 (Take Home) - due Wednesday, Feb. 26 Contingency Analysis (Ch. 9) In lab 5, part 3, you will create a mosaic plot and conducted a chi-square contingency test to evaluate whether elderly patients who did not stop walking to talk (vs. those who did stop) were more likely to suffer a fall in the next six months. I have tabulated the data below. Answer the questions below. Please show your calculations on this or a separate sheet. Did not stop walking to talk Stopped walking to talk Totals Suffered a fall Did not suffer a fall Totals 12 11 23 2 35 37 14 14 46 60 Quiz 7: 1. (2 pts) Compute the odds of falling for each group. Compute the odds ratio for those who did not stop walking vs. those who did stop walking. Interpret your result verbally.arrow_forwardSolve please and thank you!arrow_forward
- 7. In a 2011 article, M. Radelet and G. Pierce reported a logistic prediction equation for the death penalty verdicts in North Carolina. Let Y denote whether a subject convicted of murder received the death penalty (1=yes), for the defendant's race h (h1, black; h = 2, white), victim's race i (i = 1, black; i = 2, white), and number of additional factors j (j = 0, 1, 2). For the model logit[P(Y = 1)] = a + ß₁₂ + By + B²², they reported = -5.26, D â BD = 0, BD = 0.17, BY = 0, BY = 0.91, B = 0, B = 2.02, B = 3.98. (a) Estimate the probability of receiving the death penalty for the group most likely to receive it. [4 pts] (b) If, instead, parameters used constraints 3D = BY = 35 = 0, report the esti- mates. [3 pts] h (c) If, instead, parameters used constraints Σ₁ = Σ₁ BY = Σ; B = 0, report the estimates. [3 pts] Hint the probabilities, odds and odds ratios do not change with constraints.arrow_forwardSolve please and thank you!arrow_forwardSolve please and thank you!arrow_forward
- Big Ideas Math A Bridge To Success Algebra 1: Stu...AlgebraISBN:9781680331141Author:HOUGHTON MIFFLIN HARCOURTPublisher:Houghton Mifflin Harcourt
