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Regression Analysis

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Regression Analysis

Question 1

Estimate the model SalesTrop = β0 + β 1×PriceTrop + β 2×PriceMM + β 3×PriceDom + β 4×Feature + β 5×Display +

Qn1.1 The following are the regression analysis tables.

Regression Statistics
Multiple R 0.818424409
R Square 0.669818514
Adjusted R Square 0.654810265
Standard Error 11811.44376
Observations 116

 

ANOVA
  df SS MS F Significance F
Regression 5 31131717954 6.23E+09 44.63002 5.99E-25
Residual 110 15346122395 1.4E+08
Total 115 46477840349

 

  Coefficients Standard Error t Stat P-value
Intercept 54434.01686 10151.90119 5.361953 4.59E-07
PriceTrop -21274.83138 2606.694783 -8.16161 5.97E-13
PriceMM 8796.880907 2830.093242 3.108336 0.002395
PriceDom 898.2071683 3047.525898 0.294733 0.768753
Feature 938.3844498 2644.852137 0.354797 0.723421
Display 19576.16684 3195.870786 6.125456 1.43E-08

 

Qn1.2

The R-squared value of the regression is 0.669818514 implying that about 67 percent of the variation in the SalesTrop variable can be explained by the explanatory variables.

Qn1.3

The p-values of PriceDom and Feature coefficients are 0.768753 and 0.723421 respectively. Basing on the p-values, the two coefficients are statistically insignificant since the p-values are less than the significance level of 0.05.

Qn1.4

The coefficient of PriceTop is -21274.83138, while its p-value is 5.97E-13. The coefficient is statistically significant since it has a small p-value. The Negative sign on the coefficient implies that it has a negative effect on the dependent variable (William& Delvin, 1988). An increase in in PriceTop reduces the SalesTrop by that value.

Question 2

Estimating the model log(SalesTrop) = β0 + β 1×log(PriceTrop) + β 2×log(PriceMM) + β 3×Display + ε

The following are the regression analysis tables

Regression Statistics
Multiple R 0.890644868
R Square 0.793248282
Adjusted R Square 0.787710289
Standard Error 0.150713754
Observations 116

 

ANOVA
  df SS MS F Significance F
Regression 3 9.760764 3.253588 143.2375 3.51E-38
Residual 112 2.544039 0.022715
Total 115 12.3048

 

  Coefficients Standard Error t Stat P-value
Intercept 5.059883418 0.097157 52.07969 2.46E-80
log(PriceTop) -2.6047888 0.171565 -15.1825 5.96E-29
log(PriceMM) 0.559682055 0.177894 3.146146 0.002119
Display 0.277055241 0.040366 6.863575 3.92E-10

 

Qn2.2

The value of the coefficient of log(PriceTop) is -2.6047888. The value is negative and therefore implies that an increase in the scores of the variable reduces the value of log(SalesTrop) by the coefficient value. The logarithm function transforms data on the variable in a bid to view the data in a different perspective. Similarly, the transformation returns a normal-like data if then original dataset was skewed (Chao-Ying, 2002).

Qn2.3

The value of the coefficient of log(PriceMM) is 0.559682055. The value is positive, meaning that an increase in the values of the log(PriceMM) increases the value of the dependent variable.

Qn2.4

The value of the coefficient of Display is 0.277055241. The coefficient has a p-value less than the significance level of 0.05, implying that it is statistically significant. Similarly, the coefficient is positive meaning that an increase in the Display values increases the log(SalesTrop).

 

 

Works Cited

Chao-Ying Joanne Peng, Kuk Lida Lee & Gary M. Ingersoll. An Introduction to Logistic Regression Analysis and Reporting, The Journal of Educational Research, 96:1, 3-14, (2002) DOI: 10.1080/00220670209598786

Douglas Curran-Everett. Explorations in statistics: the log transformation, Advances in Physiology Education, 10.1152/advan.00018.2018, 42, 2, (343-347), (2018).

William S. Cleveland & Susan J. Devlin. Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting, Journal of the American Statistical Association, 83:403, 596-610, (1988) DOI: 10.1080/01621459.1988.10478639

 

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