Surname 1 Name Professor Course Date Tires purchased Regression analysis Regression Analysis ANOVA table Source Regression Residual Total R Adjusted R R Std. Error 0.707 0.672 0.841 2499.274 n k Dep. Var. SS 255,759,159.198 5 106,188,337.801 5 361,947,497.000 0 df MS F p-value 2 127,879,579.5992 20.47 2.97E-05 17 6,246,372.8119 Regression output variables Intercept Wheels Purchased Lugnuts Purchased 20 2 Tires Purchased 19 coefficients std. error 7,001.2145 confidence interval 95% lower 95% upper 4,672.494 0 9,329.9350 t (df=17) p-value 1,103.7555 6.343 7.35E-06 0.2809 0.0987 2.847 .0111 0.0727 0.4891 0.2677 0.1311 2.042 .0570 -0.0089 0.5442 The above table gives the results of a regression statistical analysis output. From the results, the regression equation would be; Tires Purchased=7,001.2145+0.2809 Wheels Purchased+0.2677 Lugnuts Purchased Surname 2 The coefficient of determination also called the R square is 0.707. The value indicates that 70.7% of the variation in the total number of tires purchased has been explained by the wheels purchased and lugnuts purchased. The correlation coefficient which is the value of R is 0.841. This indicates that there is a very strong positive linear correlation between the defined variables in the context. The adjusted R square is 0.672 which indicates that the model is significant and that the variables have fit well in the model. Visual presentation Below are the graphical representation of the data. Scatterplot of the tires purchased 25,000 20,000 15,000 Tires Purchased f(x) = - 442.2x + 899918.62 R = 0.36 10,000 5,000 1995 2000 2005 2010 2015 2020 Year The above is the scatterplot of the tires purchased against the year. From the graph, it is clear that we have a degreasing purchase. The trend equation is y = -442.202 x + 899,918.616 with a coefficient of determination as R = 0.359. Surname 3 Below is a box and whisker plot Descriptive statistics Wheels Purchased 20 count BoxPlot 0 5000 10000 15000 20000 25000 30000 35000 Wheels Purchased 7/25/2017 13:09.33 (1) From the above boxplot, it is clear that the data does not contain any outlier and that the data seems to be right skewed. The plot indicates that the median of the data is 12750. Surname 4 Histogram Percent 50 45 40 35 30 25 20 15 10 5 0 Lugnuts Purchased The above graph is a histogram of the lugnuts purchased. From the graph, it is clear that the data is skewed to the right and that there is no normality within the data set. Descriptive statistics count mean Tires Purchased 20 13,083.50 Wheels Purchased 20 12,678.35 Lugnuts Purchased 20 9,416.95 Above table gives the means of the defined variables. The average number of tires purchased are 13083 with 12678 wheels being purchased and 9416 lugnuts purchased. Business Scenario Predicting Tires Purchased Review the yearly data involving tires purchased at a tire shop with declining busines Scenario 5 to be in stock to have available for the customers in 2018. Topic 5 Year 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Tires Purchased 20,478 15,478 11,148 17,916 13,390 16,146 18,456 12,570 16,243 15,628 18,063 7,122 6,649 9,217 6,235 16,542 12,459 9,322 8,704 9,904 Wheels Purchased 20,018 18,274 13,086 14,659 9,697 25,698 32,237 21,404 14,993 18,346 14,193 3,052 3,740 4,748 3,662 12,426 8,743 5,243 4,687 4,661 Lugnuts Purchased 28,592 9,967 11,148 14,659 8,080 18,425 15,132 9,173 7,184 9,852 6,999 3,713 3,586 3,994 2,733 8,145 11,405 6,033 4,491 5,028 Scenario e shop with declining business. Predict how many tires will need 8