![Statistical Techniques in Business and Economics](https://www.bartleby.com/isbn_cover_images/9780077639723/9780077639723_largeCoverImage.gif)
A recent insurance industry report indicated that 40% of those persons involved in minor traffic accidents this year have been involved in at least one other traffic accident in the last 5 years. An advisory group decided to investigate this claim, believing it was too large. A sample of 200 traffic accidents this year showed 74 persons were also involved in another accident within the last 5 years. Use the .01 significance level.
- (a) Can we use z as the test statistic? Tell why or why not.
- (b) State the null hypothesis and the alternate hypothesis.
- (c) Show the decision rule graphically.
- (d) Compute the value of z and state your decision regarding the null hypothesis.
- (e) Determine and interpret the p-value.
a.
![Check Mark](/static/check-mark.png)
Check whether people can use z as the test statistic and explain the reason.
Answer to Problem 1SR
Yes, people can use z as the test statistic because both nπ and n (1–π) exceed 5.
Explanation of Solution
Requirements to check:
It is given that the sample size n is 100.
For
For
Hence, the requirements are satisfied for using the z-statistic as the binomial distribution.
b.
![Check Mark](/static/check-mark.png)
State the null and alternate hypotheses.
Explanation of Solution
In this case, the test is to check whether less than 40% of the persons involved in minor traffic accidents this year have been involved in at least one other traffic accident in the last 5 years.
Let π represents population proportion of persons involved in minor traffic accidents this year have been involved in at least one other traffic accident in the last 5 years.
Therefore, the null and alternate hypotheses are shown below:
c.
![Check Mark](/static/check-mark.png)
Show the decision rule graphically.
Explanation of Solution
Step-by-step procedure to show the decision rule graphically using MINITAB software:
- Choose Graph > Probability Distribution Plot > View Probability > OK.
- From Distribution, choose ‘Normal’ distribution.
- Enter Mean as 0 and Standard deviation as 1.
- Click the Shaded Area tab.
- Choose Probability and Left Tail for the region of the curve to shade.
- Enter the Probability as 0.01.
- Click OK.
Output using MINITAB software is obtained as follows:
From the output, the critical value is –2.326.
Therefore, the decision rule is rejecting the null hypothesis if test statistic is less than –2.326.
d.
![Check Mark](/static/check-mark.png)
Find the value of z-statistic and the write the decision regarding the null hypothesis.
Answer to Problem 1SR
The value of chi-square is –0.87.
Explanation of Solution
Calculation:
The sample size n is 200 and x is 74.
Step-by-step procedure to find the test statistic using MINITAB software:
- Choose Stat > Basic Statistics > 1 Proportion.
- Choose Summarized data.
- In Number of events, enter 74. In Number of trials, enter 200.
- Enter Hypothesized proportion as 0.40.
- Check Options, enter Confidence level as 99.0.
- Choose less than in alternative.
- Select Method as Normal approximation.
- Click OK in all dialogue boxes.
Output is obtained as follows:
From the output, the value of the test statistic is –0.87.
In this case, the critical values is –2.326 and the test statistic is –0.87.
Here, the test statistic value is less than the critical value.
That is, –0.87 > –2.326.
Therefore, do not reject the null hypothesis.
e.
![Check Mark](/static/check-mark.png)
Find and interpret the p-value.
Explanation of Solution
From the output of Part (d), it can be observed that the p-value is 0.193 and it is more than the level of significance. Therefore, there is no sufficient evidence to conclude that less than 40% of the persons involved in minor traffic accidents this year have been involved in at least one other traffic accident in the last 5 years.
Want to see more full solutions like this?
Chapter 15 Solutions
Statistical Techniques in Business and Economics
- C4 Q6 V1: Randomly collected student data in the dataset STATISTICSSTUDENTSSURVEYFORR contains the columns FEDBEST (preferred Federal party (Conservative, Green, Liberals, or NDP) ) , UNDERGORGRAD (degree being sought (GraduateProfessional, Undergraduate) ) and GENDERIDENTITY (Female or Male or Other). Make a crosstab (contingency) table of the counts for each of the (UNDERGORGRAD, FEDBEST) pairs for ONLY the females. If we randomly select a female student who is pursuing a graduateprofessional degree, what is the probability that she prefers the Federal Liberals. Choose the most correct (closest) answer below. Question 6 Answer a. 0.128 b. 0.263 c. 0.744 d. 0.333arrow_forwardInstall RStudio: Begin by installing RStudio on your computer. If you haven't done so, please refer to the official RStudio website for download and installation instructions. Watch the Tutorial Video: Watch the provided video tutorial that explains how to run RStudio. Pay close attention to the steps for opening and managing data files. https://www.youtube.com/watch?v=RhJp6vSZ7z0 Open RStudio: Once RStudio is installed, open the application. Load the Dataset: In RStudio, open a data file named "mtcars". To do this, type the command mtcars in the script editor and run the command. Attach the Data: Next, attach the dataset using the command attach(mtcars). Examine the Variables: Carefully review and note the names of all variables in the dataset. Examples of these variables include: Mileage (mpg) Number of Cylinders (cyl) Displacement (disp) Horsepower (hp) Research: Google to understand these variables. Statistical Analysis: Select mpg variable, and perform the following…arrow_forwardA marketing professor has surveyed the students at her university to better understand attitudes towards PPT usage for higher education. To be able to make inferences to the entire student body, the sample drawn needs to represent the university’s student population on all key characteristics. The table below shows the five key student demographic variables. The professor found the breakdown of the overall student body in the university’s fact book posted online. A non-parametric chi-square test was used to test the sample demographics against the population percentages shown in the table above. Review the output for the five chi-square tests on the following pages and answer the five questions: Based on the chi-square test, which sample variables adequately represent the university’s student population and which ones do not? Support your answer by providing the p-value of the chi-square test and explaining what it means. Using the results from Question 1, make recommendation for…arrow_forward
- A marketing professor has surveyed the students at her university to better understand attitudes towards PPT usage for higher education. To be able to make inferences to the entire student body, the sample drawn needs to represent the university’s student population on all key characteristics. The table below shows the five key student demographic variables. The professor found the breakdown of the overall student body in the university’s fact book posted online. A non-parametric chi-square test was used to test the sample demographics against the population percentages shown in the table above. Review the output for the five chi-square tests on the following pages and answer the five questions: Based on the chi-square test, which sample variables adequately represent the university’s student population and which ones do not? Support your answer by providing the p-value of the chi-square test and explaining what it means. Using the results from Question 1, make recommendation for…arrow_forwardA retail chain is interested in determining whether a digital video point-of-purchase (POP) display would stimulate higher sales for a brand advertised compared to the standard cardboard point-of-purchase display. To test this, a one-shot static group design experiment was conducted over a four-week period in 100 different stores. Fifty stores were randomly assigned to the control treatment (standard display) and the other 50 stores were randomly assigned to the experimental treatment (digital display). Compare the sales of the control group (standard POP) to the experimental group (digital POP). What were the average sales for the standard POP display (control group)? What were the sales for the digital display (experimental group)? What is the (mean) difference in sales between the experimental group and control group? List the null hypothesis being tested. Do you reject or retain the null hypothesis based on the results of the independent t-test? Was the difference between the…arrow_forwardWhat were the average sales for the four weeks prior to the experiment? What were the sales during the four weeks when the stores used the digital display? What is the mean difference in sales between the experimental and regular POP time periods? State the null hypothesis being tested by the paired sample t-test. Do you reject or retain the null hypothesis? At a 95% significance level, was the difference significant? Explain why or why not using the results from the paired sample t-test. Should the manager of the retail chain install new digital displays in each store? Justify your answer.arrow_forward
- A retail chain is interested in determining whether a digital video point-of-purchase (POP) display would stimulate higher sales for a brand advertised compared to the standard cardboard point-of-purchase display. To test this, a one-shot static group design experiment was conducted over a four-week period in 100 different stores. Fifty stores were randomly assigned to the control treatment (standard display) and the other 50 stores were randomly assigned to the experimental treatment (digital display). Compare the sales of the control group (standard POP) to the experimental group (digital POP). What were the average sales for the standard POP display (control group)? What were the sales for the digital display (experimental group)? What is the (mean) difference in sales between the experimental group and control group? List the null hypothesis being tested. Do you reject or retain the null hypothesis based on the results of the independent t-test? Was the difference between the…arrow_forwardQuestion 4 An article in Quality Progress (May 2011, pp. 42-48) describes the use of factorial experiments to improve a silver powder production process. This product is used in conductive pastes to manufacture a wide variety of products ranging from silicon wafers to elastic membrane switches. Powder density (g/cm²) and surface area (cm/g) are the two critical characteristics of this product. The experiments involved three factors: reaction temperature, ammonium percentage, stirring rate. Each of these factors had two levels, and the design was replicated twice. The design is shown in Table 3. A222222222222233 Stir Rate (RPM) Ammonium (%) Table 3: Silver Powder Experiment from Exercise 13.23 Temperature (°C) Density Surface Area 100 8 14.68 0.40 100 8 15.18 0.43 30 100 8 15.12 0.42 30 100 17.48 0.41 150 7.54 0.69 150 8 6.66 0.67 30 150 8 12.46 0.52 30 150 8 12.62 0.36 100 40 10.95 0.58 100 40 17.68 0.43 30 100 40 12.65 0.57 30 100 40 15.96 0.54 150 40 8.03 0.68 150 40 8.84 0.75 30 150…arrow_forward- + ++ Table 2: Crack Experiment for Exercise 2 A B C D Treatment Combination (1) Replicate I II 7.037 6.376 14.707 15.219 |++++ 1 བྱ॰༤༠སྦྱོ སྦྱོཋཏྟཱུ a b ab 11.635 12.089 17.273 17.815 с ас 10.403 10.151 4.368 4.098 bc abc 9.360 9.253 13.440 12.923 d 8.561 8.951 ad 16.867 17.052 bd 13.876 13.658 abd 19.824 19.639 cd 11.846 12.337 acd 6.125 5.904 bcd 11.190 10.935 abcd 15.653 15.053 Question 3 Continuation of Exercise 2. One of the variables in the experiment described in Exercise 2, heat treatment method (C), is a categorical variable. Assume that the remaining factors are continuous. (a) Write two regression models for predicting crack length, one for each level of the heat treatment method variable. What differences, if any, do you notice in these two equations? (b) Generate appropriate response surface contour plots for the two regression models in part (a). (c) What set of conditions would you recommend for the factors A, B, and D if you use heat treatment method C = +? (d) Repeat…arrow_forward
- Question 2 A nickel-titanium alloy is used to make components for jet turbine aircraft engines. Cracking is a potentially serious problem in the final part because it can lead to nonrecoverable failure. A test is run at the parts producer to determine the effect of four factors on cracks. The four factors are: pouring temperature (A), titanium content (B), heat treatment method (C), amount of grain refiner used (D). Two replicates of a 24 design are run, and the length of crack (in mm x10-2) induced in a sample coupon subjected to a standard test is measured. The data are shown in Table 2. 1 (a) Estimate the factor effects. Which factor effects appear to be large? (b) Conduct an analysis of variance. Do any of the factors affect cracking? Use a = 0.05. (c) Write down a regression model that can be used to predict crack length as a function of the significant main effects and interactions you have identified in part (b). (d) Analyze the residuals from this experiment. (e) Is there an…arrow_forwardA 24-1 design has been used to investigate the effect of four factors on the resistivity of a silicon wafer. The data from this experiment are shown in Table 4. Table 4: Resistivity Experiment for Exercise 5 Run A B с D Resistivity 1 23 2 3 4 5 6 7 8 9 10 11 12 I+I+I+I+Oooo 0 0 ||++TI++o000 33.2 4.6 31.2 9.6 40.6 162.4 39.4 158.6 63.4 62.6 58.7 0 0 60.9 3 (a) Estimate the factor effects. Plot the effect estimates on a normal probability scale. (b) Identify a tentative model for this process. Fit the model and test for curvature. (c) Plot the residuals from the model in part (b) versus the predicted resistivity. Is there any indication on this plot of model inadequacy? (d) Construct a normal probability plot of the residuals. Is there any reason to doubt the validity of the normality assumption?arrow_forwardStem1: 1,4 Stem 2: 2,4,8 Stem3: 2,4 Stem4: 0,1,6,8 Stem5: 0,1,2,3,9 Stem 6: 2,2 What’s the Min,Q1, Med,Q3,Max?arrow_forward
- Glencoe Algebra 1, Student Edition, 9780079039897...AlgebraISBN:9780079039897Author:CarterPublisher:McGraw HillCollege Algebra (MindTap Course List)AlgebraISBN:9781305652231Author:R. David Gustafson, Jeff HughesPublisher:Cengage LearningHolt Mcdougal Larson Pre-algebra: Student Edition...AlgebraISBN:9780547587776Author:HOLT MCDOUGALPublisher:HOLT MCDOUGAL
![Text book image](https://www.bartleby.com/isbn_cover_images/9780079039897/9780079039897_smallCoverImage.jpg)
![Text book image](https://www.bartleby.com/isbn_cover_images/9781305652231/9781305652231_smallCoverImage.gif)
![Text book image](https://www.bartleby.com/isbn_cover_images/9780547587776/9780547587776_smallCoverImage.jpg)