c. Use MS Excel Data Analysis ToolPak to perform two (2) simple regressions, one using Quality as the response variable and Helpfulness as the predictor variable (Model 1) and the other using Quality as the response variable and raterInterest as the predictor variable (Model 2). Compare the two models in terms of R2 value. Which of these two variables is a better predictor of Quality? Explain why.
| model 1 | model 2 | |||
| Regression Statistics | Regression Statistics | |||
| Multiple R | 0.981031 | Multiple R | 0.470669 | |
| R Square | 0.962423 | R Square | 0.221529 | |
| Adjusted R Square | 0.962319 | Adjusted R Square | 0.21939 | |
| Standard Error | 0.163498 | Standard Error | 0.488182 | |
| Observations | 366 | Observations | 366 | |
Solution: The R-square value for model 1 is 0.9624, therefore, 96.24% of the variation in the dependent variable Quality is explained by the predictor variable Helpfulness.
The R-square for model 2 is 0.2215, therefore, 22.15% of the variation in the dependent variable Quality is explained by the predictor variable rater interest.
Since the R-square for mode1 is more than the R-square for model 2, therefore, Helpfulness is a better predictor of Quality.
c. Use MS Excel Data Analysis ToolPak to perform two (2) simple regressions, one using Quality...
g. Use MS Excel Data Analysis ToolPak to perform a multiple regression analysis using Quality as the response variable and Helpfulness, Clarity, Easiness, and raterInterest as the explanatory variables. Write down the resulting regression equation and provide the regression output. h. Based on the regression output in part g), which variable(s) seem to be significant predictors of Quality? Which variable(s) do you suggest removing from the model in part g)? Explain why. Regression Statistics ANOVA Multiple R 0.998557685 df SS...
Linear Regression: Use Data Analysis in Excel to conduct the Regression Analysis to reproduce the excel out put below (Note: First enter the data in the next page in an Excel spreadsheet) Home Sale Price: The table below provides the Excel output of a regression analysis of the relationship between Home sale price(Y) measured in thousand dollars and Square feet area (x): SUMMARY OUTPUT Dependent: Home Price ($1000) Regression Statistics Multiple R 0.691 R Square 0.478 Adjusted R Square 0.465...
You estimate the demand function for soft drinks using a multiple regression model. The MS Excel printout for the regression follows: SUMMARY OUTPUT Regression Statistics Multiple R 0.835478305 R Square 0.698023997 Adjusted R Square 0.677434724 Standard Error 38.26108281 Observations 48 ANOVA df SS MS F Significance F Regression 3 148889.8565 49629.95217 33.9023141 1.64557E-11 Residual 44 64412.06016 1463.910458 Total 47 213301.9167 Coefficients Standard Error t Stat P-value Intercept 514.2669369 113.3315243 4.537721874 4.36383E-05 6 pack price 242.9707509 43.52628127 5.582161944 1.38245E-06 mean temp...
Consider the following Excel multiple regression of output of Total Sales on the (c) other (predictor) variables. Provide some important arguments about the fitted multiple regression model. (Give one argument about each of the three main outputs.) [4 marks] SUMMARY OUTPUT Regression Statistics Multiple R 0.9870 R Square Adjusted R Square 0.9741 0.9721 Standard Error 116.2766 Observations 43 ANOVA Significance F df SS MS F Regression 19817036.22 6605678.74 488.58 5.82876E-31 Residual 527289.46 39 13520.24 Total 42 20344325.68 P-value Coefficients Standard...
HW # 5 Linear Regression: Use Data Analysis in Excel to conduct the Regression Analysis to reproduce the excel out put below (Note: First enter the data in the next page in an Excel spreadsheet) Home Sale Price: The table below provides the Excel output of a regression analysis of the relationship between Home sale price(Y) measured in thousand dollars and Square feet area (x): SUMMARY OUTPUT Dependent: Home Price ($1000) Regression Statistics Multiple R 0.691 R Square 0.478 Adjusted...
(a) The following is taken from the output generated by an Excel analysis of expenditure data using multiple regression: Regression Statistics Multiple R 0.9280 0.8611 0.8365 Adjusted R2 Standard Error.1488 Observations21 ANOVA Source Regression Residual Total df MS Significance of F 1.66E-07 3 308.68 35.117 102.893 2.930 17 20 358.49 49.81 Coefficient Standard Error 6.2000 0.7260 0.7260 0.9500 t Stat 3.7097 0.2755 -2.0523 0.5158 23.00 0.20 Intercept X2 X3 0.49 (i) Find the limits of the 95 percent confidence interval...
Pyrene was analyzed using GC-MS at the selected ion mode. The calibration curve of peak area (GC-MS response) vs. concentration is shown below along with the regression output. A blank sample was analyzed seven times, giving a mean peak area of 125 and standard deviation of 15. A check standard at 0.5 ppm was also measured seven times, giving a standard deviation of 300. The raw data, Excel's printouts of linear regression along with the line plot are attached. (a)...
Use Excel to develop a regression model for the Hospital
Database (using the “Excel Databases.xls” file on Blackboard) to
predict the number of Personnel by the number of Births. What can
you conclude from the study?
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.697463374
R
Square
0.486455158
Adjusted R Square
0.483861497
Standard Error
590.2581194
Observations
200
ANOVA
df
SS
MS
F
Significance F
Regression
1
65345181.8
65345181.8
187.5554252
1.79694E-30
Residual
198
68984120.2
348404.6475
Total
199
134329302
Coefficients
Standard Error
t Stat...
4. Part of an Excel output relating X (independent variable) and Y (dependent variable) is shown below. Fill in all the blanks marked with "?". Summary Output Regression Statistics Multiple R ? R Square ? Adjusted R Square 0.8125 Standard Error 1.3693064 Observations 7 ANOVA df SS MS F Significance F Regression ? 50.625 ? ? ? Residual ? 9.375 ? Total 6 60 Coefficients Standard Error. t Stat P-value Lower 95% Intercept 13.75 1.398341. 9.833082 0.0001853 10.15555 x -1.125...
To examine the differences between salaries of male and female middle managers of a large bank, 90 individuals were randomly selected, and two models were created with the following variables considered Salary- the monthly salary (excluding fringe benefits and bonuses), Educ the number of years of education, Exper the number of months of experience, Train the number of weeks of training, Gender- the gender of an individual; 1 for males, and O for females. Excel partial outputs corresponding to these...