Excel Project Example

A regression analysis was performed and the summary output is shown below.   Regression Statistics Multiple R 0.7193267530.719326753 R Square 0.5174309770.517430977 Adjusted R Square 0.4991055710.499105571 Standard Error 8.6995238488.699523848 Observations 165165     ANOVA   dfdf SSSS MSMS F� Significance F� Regression 66 12,821.56512,821.565 2136.9282136.928 28.235728.2357 8.9037E-238.9037E-23 Residual 158158 11,957.71111,957.711 75.68275.682     Total 164164 24,779.27624,779.276           Step 1 of 2 :   How many independent variables are included in the regression model

Analyse the following regression  Regression Statistics               Multiple R 0.79716916               R Square 0.63547867               Adjusted R Square 0.63254686               Standard Error 198.375358               Observations 377                                 ANOVA                   df SS MS F Significance F       Regression 3 25589530 8529843.34 216.753245 2.2501E-81       Residual 373 14678587.9 39352.7826           Total 376 40268117.9                                 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -97.981174 79.2847437 -1.2358137 0.21730553 -253.88228 57.9199296 -253.88228 57.9199296 EquivArea 3.5523258 0.15149067 23.4491399 2.2549E-75 3.254443 3.85020861 3.254443 3.85020861 Years 2.04629486 0.31770115 6.44094253 3.6636E-10 1.42158501 2.67100471 1.42158501 2.67100471 Condition 15.0379067 10.2144479 1.47221924 0.14180492 -5.0472147 35.1230282 -5.0472147 35.1230282

A regression analysis was performed and the summary output is shown below.   Regression Statistics Multiple R 0.7802268560.780226856 R Square 0.6087539470.608753947 Adjusted R Square 0.5870180550.587018055 Standard Error 6.7217061336.721706133 Observations 20     ANOVA   dfdf SSSS MSMS F� Significance F� Regression 11 1265.3871265.387 1265.3871265.387 28.006928.0069 4.9549E-054.9549E-05 Residual 1818 813.264813.264 45.18145.181     Total 1919 2078.6512078.651           Step 2 of 2:  Which measure is appropriate for determining the proportion of variation in the dependent variable explained by the set of independent variable(s) in this model?

A sales manager for an advertising agency believes there is a relationship between the number of contacts that a salesperson makes and the amount of sales dollars earned. A regression ANOVA shows the following results:ANOVA   df SS MS F Significance F Regression 1.00 13,555.42 13,555.42 156.38 0.00 Residual 8.00 693.48 86.88     Total 9.00 14,248.90       What is the value of the standard error of estimate?   Multiple Choice   9.321   8.789   8.339   86.88

3. You are working on a regression when accidentally you dump coffee all over yourregression output. Using the remaining values, find the missing values in each blank numbered 1-9 SUMMARY OUTPUT Regression Statistics Multiple R 0.860644193 R Square # 4 Adjusted R Square 0.72282625 Standard Error #5 Observations 32 ANOVA df MS F Significance F Regression 2 834.0726419 417.0363209 41.4216016 3.16178E-09 Residual #2 #1 # 3 Total 31 1126.047188 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 36.90833048 2.190798638 16.84697527 1.62066E-16 32.42764417 41.38901679 cyl -2.264693597 0.575889243 #7 0.000480375 -3.442519346 -1.086867847 hp #6 0.01500073 -1.274717745 0.212528465 #8 #9 Screenshot

Analyse the following regression model Regression Statistics               Multiple R 0.7958395               R Square 0.63336051               Adjusted R Square 0.63139987               Standard Error 198.684728               Observations 377                                 ANOVA                   df SS MS F Significance F       Regression 2 25504235.6 12752117.8 323.037801 3.2564E-82       Residual 374 14763882.3 39475.6211           Total 376 40268117.9                                 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 6.10606259 35.9370414 0.16991 0.86517279 -64.557919 76.770044 -64.557919 76.770044 EquivArea 3.62347902 0.1437982 25.1983622 1.3118E-82 3.34072471 3.90623332 3.34072471 3.90623332 Years 1.90428034 0.30317478 6.28113036 9.3418E-10 1.30813952 2.50042116 1.30813952 2.50042116

What is the R2?     What is the adjusted R2?     Explain the purpose of having an adjusted R2?

Bailey thinks her heartrate will increase as she increases her biking speed. To see if this relationship exists, she records seven different speeds and models it with a scatterplot and regression output. Pulse, beats per minute 130 120 110 100 90 80 70 7 7.5 8 8.5 9 9.5 10 10.5 11 11.5 12 12.5 13 Speed, miles per hour Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 97.50557% 95.07336% 94.08803% 3.884894879 7

Verdadero=true falso=false

Conduct a global test on the set of independent variables. Interpret   Regression Statistics               Multiple R 0.87027387               R Square 0.75737661               Adjusted R Square 0.75615535               Standard Error 14.6932431               Observations 600                                 ANOVA                   df SS MS F Significance F       Regression 3 401662.063 133887.354 620.160683 8.708E-183       Residual 596 128671.271 215.891394           Total 599 530333.333                                 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -3.9995369 3.05935528 -1.3073137 0.19161031 -10.007965 2.00889078 -10.007965 2.00889078 Annual Income 0.0002132 3.1402E-05 6.78944156 2.7269E-11 0.00015153 0.00027487 0.00015153 0.00027487 Married 45.7808695 1.20203164 38.0862434 8.444E-162 43.4201368 48.1416023 43.4201368 48.1416023 Male 21.9175699 1.20122625 18.2459964 2.0045E-59…

A multiple regression analysis produced the following output from Excel. Summary Output                     Regression Statistics                     Multiple R   0.978724022                 R Square   0.957900711                         Adjusted R Square   0.952287472                 Standard Error   67.67055418                 Observations   18                                             ANOVA                         df   SS   MS   F   Significance F Regression   2   1562918.941   781459.5   170.6503   4.80907E-11 Residual   15   68689.55855   4579.304         Total   17   1631608.5                                       Coefficients   Standard Error   t Stat   P-value     Intercept   1959.709718   306.4905312   6.39403   1.21E-05     X1   -0.469657287   0.264557168   -1.77526   0.096144     X2   -2.163344882   0.278361425   -7.77171   1.23E-06     The overall…

B. Would the Floor Area be a significant predictor of Assessed Value?

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.

Would the Age be a significant predictor of Assessed Value?

Consider the following summary output, which was generated from a sample of 8 employees relating age to annual salary. SUMMARY OUTPUT Regression Statistics Multiple R 0.138089232 R Square 0.019068636 Adjusted R Square -0.144419925 Standard Error 1.46902996 Observations 8. ANOVA df SS MS F Regression 0.251706 0.252 0.116775 Residual 6. 12.948294 2.158 Total 13.2 wkes Learning