Table Of Contents
  • Online business statistics homework help for papers derived from descriptive statistics
  • Business statistics assignment help with regression analysis
  • Learn more about data visualization from our business statistics tutors

Online business statistics homework help for papers derived from descriptive statistics

Variable Names

Minimum

Maximum

Mean

Std. Deviation

Skewness

Kurtosis

Statistic

Statistic

Statistic

Std. Error

Statistic

Statistic

Std. Error

Statistic

Std. Error

 

Sales in thousands

.110

540.561

52.99808

5.429339

68.029422

3.409

.194

17.557

.385

 

Log-transformed sales

-2.21

6.29

3.2959

.10520

1.31821

-.821

.194

3.242

.389

 

Price in thousands

9.235

85.500

27.39075

1.152753

14.351653

1.766

.195

3.630

.387

 

Horsepower

55

450

185.95

4.540

56.700

1.001

.194

2.407

.386

 

Curb weight

1.895

5.572

3.37803

.050643

.630502

.708

.195

1.265

.387

 

Fuel efficiency

15

45

23.84

.345

4.283

.693

.195

3.242

.389

 


The sales of cars in thousands computed a mean value of 52.9981 with 68.0294 standard deviations. The sales of cars in thousands appear to be skewed to the right as the skewness was computed to be equal to 3.406 with 0.194 standard deviations. There also appears to be widespread in the variable as a large gap between the minimum, mean and maximum values.
The price of the car in thousands, Horsepower, curb weight, and fuel-efficient computed a mean value of 27.3907, 185.95, 3.378, and 23.84 with 14.3516, 56.700, 0.6305, and 4.283 standard deviations respectively. Price and horsepower are positively skewed while curb weight and fuel efficiency appear to be normal. There also appears to be no widespread in the variable as a large gap between the minimum, mean and maximum values.
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Business statistics assignment help with regression analysis

The Multiple regression model was used to examine if the sales of cars can be predicted using key predictors that make vehicles desirable, the assumption of the test which is a Linear relationship, multicollinearity, and Homoscedasticity was already examined using different mediums ranging from the correlation matrix to the construction of scatter diagram. The assumptions were already validated as established from the different diagnostic tests conducted and reported earlier. The regression model above presents the tests of the study hypothesis. The p-value for the full model was observed to be less than 0.0001, which is an indication that the test is significant at a 0.05 level of significance. This test rejects the null hypothesis and concludes that at least one of the key predictors that make vehicles desirable significantly predicts the sales of cars in thousands.

Summarily, it can be observed from the series of analyses conducted that the sales of cars in thousand are distributed with mean 52.9981 and 68.0294 levels of dispersion, the price is distributed with mean 27.3707 and 14.3516 standard deviations while the fuel efficiency is distributed with mean 23.84 and 4.283 standard deviations.

This analysis aimed to study if the key predictors that make vehicles desirable can be used to determine their sales of cars. To answer the research question, the SPSS car sales dataset was used to predict vehicle sales from what is believed to be key predictors that make vehicles desirable. These include type, price, horsepower, curb weight, and fuel efficiency. However, after realizing that some of these expectations may not hold, given that powerful engines and big vehicles are more expensive to make and less fuel-efficient and that sales are a highly skewed variable. The analysis used the log of sales as your criterion and that the assumptions of the multiple linear regression model will not be violated in the course of estimating the regression model. When examining whether price significantly predicts the sales of vehicles, the results of the regression model indicated that price is the only significant predictor of car sales as it explains 15.4% of the variation in sales of vehicles (R2=0.154, F (5,146) =5.313, p<.0001).

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Business Statistics Homework Help 9

From the SDFit and SDBeta values using the Explore function and box plot, we can observe that the F-series model shows up as extreme outliers across different tests as its influence appear to be greater |1.0|. This implies that the F-series model can be regarded as a cause for concern about exerting excessive influence on the regression model.

Business Statistics Homework Help 10

From the plot of the predicted and residual values above, our business statistics tutors argue that there is a concern for multicollinearity in the model as the scattered diagram revealed that the spread of the residuals plotted against the predicted value is not scattered. That is, the residuals were getting more significant as the predicted value increased which is a visual indication of the violation of equal variance assumption. If you would like to hire our business statistics writers for help with this topic, reach out to us immediately. We will connect you with one of our best experts so you can receive the most accurate academic solutions.