SUMMARY OUTPUTRegression StatisticsMultiple RR-Square0.971AAdjusted R-Square BStandard Error30.462Observations51ANOVASignificance FRegression C 747851.57373925.79 402.989.89E-31df SSMSFResidual 48 D927.91Total50792391.11StandardLowerUpperCoefficientst Stat P-ValueError95%95%Intercept62.1326.79 1.60E-301539.661789.51Price of Roses-6.68F-1.411.64E-01-16.162.81Disposable Income(M)9.730.34G1.23E-319.0410.42Determine the t-statistic of the estimated slope coefficient for disposable income(point G) and whether that estimated slope coefficient is statistically significant atthe 5 percent level.28.62 and statistically significant since the P-value is less than 5 percent3.31 and statistically significant since the t-statistic is greater than 2 in absolutevalue3.31 and statistically insignificant since the P-value is less than 5 percent0.03 and statistically insignificant since the t-statistic is less than 2 in absolutevalue

Estimated coefficient is 9.73 and standard error is 0.34.

Answered: Regression Statistics Multiple R…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

linear refression
pre-test post-test Mean Variance -2.25 0.75 9.30 13.30 Observations 12 12 Pearson Correlation 0.9478 Hypothesized Mean 0 Difference df 11 t Stat 2.076 P(T<=t) one-tail 0.035 t Critical one-tail 1.796 P(T<=t) two-tail 0.065 t Critical two-tail 2.201
SUMMARY OUTPUT Regression Statistics Multiple R 0.664798 R Square 0.441957 Adjusted R Square 0.376305 Standard Error 6.412199 Observations 20 ANOVA df SS MS F Significance F Regression 2 553.5729 276.7864 6.731793 0.007025498 Residual 17 698.9771 41.1163 Total 19 1252.55 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 26.6651 13.92768 1.91454 0.072535 -2.71974085 56.04995 X1 4.00929 1.1224 3.572068 0.002347 1.641232912 6.377348 X2 0.810165 0.477768 1.69573 0.108172 -0.19783686 1.818168 a. What can you say about the strength of this relationship for the model using the F test? Use α = .05. b. Is y significantly related to each independent variable? Use α = .05. c. Would your answer to b change if α = .001? If so, how? (3+4+3)
An educational psychologist is examining response times to an on-screen stimulus. The researcher believes there might be a weak effect from age, but expects a more pronounced effect for different color contrasts. She decides to examine a black on white (B/W) combination compared to 2 alternatives: red on white (R/W) and yellow on blue (Y/B). Here is the data for response times (in milliseconds): Color Scheme B/W R/W Y/B 10 17 23 26 44 44 22 21 25 22 21 4 16-17 11 32 34 31 24 20 16 20 21 36 8 8 24 37 23 11 39 36 23 17 28 32 36 31 Age 18-19 19 15 45 15 35 36 13 47 23 16 19 31 24 30 25 16 40 16 27 25 42 23 28 35 20-21 21 18 42 27 19 39 39 28 27 18 26 38 Using MS Excel, conduct a 2-way ANOVA with a = 0.05. Fill in the summary table. The last column in the table below is read as, "Partial Eta-squared " and is a concept that was NOT covered in the lectures. Partial n2 is used when there is a chance the data is not independent (click here to read more if interested 2) Computing the values for…
P-values should be accurate to 4 decimal places and all other values accurate to 3 decimal places. Source S df MS F-ratio P-value Partial n? Age (A) 2 Color (B) Interaction (A × B) 4 Error 63 Provide the conclusions for this 2-way ANOVA. What is the conclusion regarding the main effect for age (A or rows): O There appears to be an effect due to age. O There is not enough evidence to reject the assumption that the marginal means across age groups are equal. What is the conclusion regarding the main effect for color (B or columns): There appears to be an effect due to color. There is not enough evidence to reject the assumption that the marginal means across color groups are equal. What is the conclusion regarding an interaction effect between age and color (A x B): There appears to be an interaction effect between age and color. There is not enough evidence to reject the assumption of a lack of an interaction.
ANOVA df SS MS F Significance F Regression 1 7195512474 7195512474 6.504373807 0.012309437 Residual 98 1.08413E+11 1106257526 Total 99 1.15609E+11 Please explain this...
The key assumptions of the ANOVA include homogeneity of variance, normality of population distributions and: Independence of observations The DV must be measured on a nominal level The IV must be measured on a continuous level Variability of residuals
Excessive fertilizer and surface runoff can result in heavy phosphorus concentrations and reduced oxygen levels in lakes and ponds. This can lead to die-off of insects, fish, an increase in algae, and overall reduced health of the ecosystem. With the increase in algae, you are concerned about the amount of dissolved oxygen (DO). You measure the average DO levels in randomly sampled lakes in two different types (urban and rural) at the same time of year. Conduct a hypothesis test at 5% level of significance. Is there evidence to suggest that the average DO levels are significantly higher in lakes found in rural areas when compared to lakes in more urban areas? Use the table below. What is the test statistic?
Match the regression (linear model) assumptions with the corresponding graphical procedure for testing that assumption. ( Choose ] Std. residuals vs fitted values plot Normality v QQ plot Time Series plot (of responses or residuals) Independence Std. residuals vs fitted values Homogeneity of variance Time Series plot (of responses v Model fit Std. residuals vs fitted values v
Are the heights of individuals affected by the heights of their parents. Regression Statistics Multiple R R Square Adjusted R Squar 0.631071992 0.398251859 0.365724932 Standard Error 2.914527039 Observations 40 ANOVA df MS F Significance F Regression Residual 2 208.0084392 104.0042 12.24376 8.30181E-05 37 314.2953108 8.494468 Total 39 522.30375 Coefficients Standard Error t Stat P-value Intercept Mother's Height Father's Height 9.804326378 12.39987353 0.79068 0.43417 0.657952815 0.147476295 4.461414 7.34E-05 0.200358437 0.138223638 1.449524 0.155615 1. Write the regression equation that represents the above equation. 2. Is this a good predictor equation? Why or why not (use appropriate statistics/hypothesis test to prove your point)? 3. Use the equation to predict the height of someone whose mother is 52 inches tall and whose father is 70 inches tall.
What is a residual for a multiple regression model and the data that is used to create it? Select one. Question 3 options: A statistic that explains the relationship between response and predictor variables The predicted value of the response variable using the multiple regression model A statistic that is used to evaluate the significance of the multiple regression model The difference between the actual value of the response variable and the corresponding predicted value (regression error) using the multiple regression model
Data were collected to explain the number of wins an NFL team has based on the average points per game that they score (PPG) *COMPLETE THE TABLE BELOW*

SEE MORE QUESTIONS

Recommended textbooks for you

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

Elementary Statistics: Picturing the World (7th E…

Statistics

ISBN:

9780134683416

Author:

Ron Larson, Betsy Farber

Publisher:

PEARSON

The Basic Practice of Statistics

Statistics

ISBN:

9781319042578

Author:

David S. Moore, William I. Notz, Michael A. Fligner

Publisher:

W. H. Freeman

Introduction to the Practice of Statistics

Statistics

ISBN:

9781319013387

Author:

David S. Moore, George P. McCabe, Bruce A. Craig

Publisher:

W. H. Freeman

SEE MORE TEXTBOOKS