Multiple Regression Analysis
This information is taken from 80 homes recently sold along the Gulf of Mexico coast.Analyze the data to discover which of the variable have a statistically significant influence on the sales price.
A. Write out the equation for the model you develop
B, Interpret the equation as a model and the meaning of the information for each variable in your "best" model
C.Interpret the confidence intervals for each of the statistically significant variables
Use the data provided below
SUMMARY OUTPUT | ||||||||
Regression Statistics | ||||||||
Multiple R | 0.935126708 | |||||||
R Square | 0.874461961 | |||||||
Adjusted R Square | 0.867766599 | |||||||
Standard Error | 13.53205217 | |||||||
Observations | 80 | |||||||
ANOVA | ||||||||
df | SS | MS | F | Significance F | ||||
Regression | 4 | 95665.24119 | 23916.31 | 130.60712 | 5.40622E-33 | |||
Residual | 75 | 13733.73269 | 183.1164 | |||||
Total | 79 | 109398.9739 | ||||||
Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Lower 95.0% | Upper 95.0% | |
Intercept | 24.97603818 | 16.62666277 | 1.502168 | 0.137252656 | -8.145972546 | 58.0980489 | -8.145972546 | 58.0980489 |
Size | 0.052635722 | 0.00659448 | 7.981785 | 1.29238E-11 | 0.039498845 | 0.0657726 | 0.039498845 | 0.0657726 |
Number of | 10.04302252 | 3.728710007 | 2.693431 | 0.008720668 | 2.615051286 | 17.47099376 | 2.615051286 | 17.47099376 |
Niceness | 10.04203197 | 0.791493985 | 12.68744 | 2.37694E-20 | 8.465295102 | 11.61876885 | 8.465295102 | 11.61876885 |
Pool? | 25.86232229 | 3.574712124 | 7.234799 | 3.36199E-10 | 18.74113057 | 32.98351402 | 18.74113057 | 32.98351402 |
Multiple Regression Analysis This information is taken from 80 homes recently sold along the Gulf of...
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...
From the regression example discussed in class and based on the information below: Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.925 0.856 0.846 0.059 45 ANOVA P dfss SMS 3 0 .85 0.14 440.99 Significance F 0.00 Regression Residual Total 0.28 0.00 81.46 Intercept PRICE INCOME WEATHER Coefficients 13.040 -0.200 1.500 0.124 Standard Error 0.758 0.063 0.079 0.065 Stat P-value 17.1940 .000 -7.904 0.000 13.162 0.000 1.909 0.063 L ower 95% 11.508 -0.627 0.883 -0.007...
Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.72 0.51 0.38 99.45 6 Anova df SS MS F Significance F 0.11 1 41497.60 41497.60 4.20 Regression Residual 4 39561.23 9890.31 Total 5 81058.83 t Stat P-value Coefficients Standard Error 1423.60 564.95 2.52 0.07 Intercept X Variable 1 Lower 95% Upper 95% -144.96 2992.16 -0.11 0.72 Lower 95.0% Upper 95.0% -144.96 2992.16 -0.11 0.72 0.31 0.15 2.05 0.11 Assume that Craig's Fresh and Hot Pancake Restaurant does...
SUMMARY OUTPUT Regression Statistics Multiple R 0.818616296 R Square 0.67013264 Adjusted R Square 0.658351663 Standard Error 9.16867179 Observations 30 ANOVA df SS MS F Significance F Regression 1 4781.80995 4781.80995 56.8826 3.2455E-08 Residual 28 2353.807187 84.06454239 Total 29 7135.617137 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 28.21496731 3.739591617 7.544932763 3.22E-08 20.55476114 35.87517349 Dividend 2.367177613 0.313863719 7.542055589 3.25E-08 1.724256931 3.010098296 c. You run a regression analysis using Data Analysis to answer the following question: Is stock selling...
SUMMARY OUTPUT Regression Statistics Multiple R 0.985689515 R Square 0.97158382 Adjusted R Square 0.968940454 Standard Error 754.6653051 Observations 48 ANOVA df SS MS F Significance F Regression 4 837320651.9 209330163 367.555599 1.23563E-32 Residual 43 24489348.08 569519.723 Total 47 861810000 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -979.9824986 2587.408411 -0.3787506 0.70673679 -6197.988856 4238.02386 -6197.988856 4238.023859 Price (cents) -39.65930534 3.380682944 -11.731152 5.4685E-15 -46.47710226 -32.841508 -46.47710226 -32.84150842 Competitors Price (cents) 39.71320378 3.717321495 10.6832847 1.1179E-13 32.21651052 47.209897...
In relation to the below output from the Regression Analysis of the S&P/ASX200 Index (X) and from the company ABC Shares derived from weekly data over a 12 month period, can you please explain the key measures and what this all means eg. Number of Observations, R Square, Value of the Slope and the P-Value of the Slope etc. SUMMARY OUTPUT Regression Statistics Multiple R 0.045274332 R Square 0.002049765 Adjusted R Square -0.01790924 Standard Error 0.023996449 Observations 52 ANOVA df...
We are doing regression analysis for business analytics class and I am having a hard time reading this data. Please help. SUMMARY OUTPUT Regression Statistics Multiple R 0.999964 R Square 0.999928 Adjusted R Square 0.9999248 Standard Error 267.074107 Observations 48 ANOVA df SS MS F Significance F Regression 2 44576676715 2.23E+10 312474.2 6.1672E-94 Residual 45 3209786.045 71328.58 Total 47 44579886501 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -42159057 121894.4727 -345.865 1.04E-78 -42404564.6...
Problem 5- Simple Linear Regression The following data represent the number of flash drives sold per day at a local computer shop and their prices Price $34 36 32 35 30 Units Sold 6 40 A computer output is produced to examine this relationship further SUMMA RY OUTPUT Regression Statistics Multiple R RSquare Adjusted R Square Standard Error Observations 0.924982 0.855592 0.826711 1.119949 7 ANOVA MS gnificance F Regression Residual Total 137.15714 37.15714 29.62415 0.002842 5 б,271429 1.254286 6 43.42857...
1.Based on the table above, how to intepret this regression analysis? 2. When we need to look at the adjusted r2 and why? 3. How to conduct the hypothesis test? 0 Regression Statistics 1 Multiple R 2 R Square 3 Adjusted RS 0.853658537 0,97530483 0.951219512 4 Standard Err 0.191273014 5 Observation 6 7 ANOVA Significance F 0.220863052 df SS MS 0.713414634 0.356707 9 Regression 0 Residual 1 Total 2. 9.75 1 0.036585366 0.036585 0.75 2 Lower 95 % 3 Coefficients...
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...