The following data shows the increase in Halloween candy sales over a 7-year period. Halloween Candy Sales (in million of dollars) Year Candy Sales a. Describe the association between candy sales and year. type your answer... b. Write the regression equation for predicting candy sales from year. candy type your answer... = c. Explain the meaning of the slope. type your answer... year d. Predict the amount of sales for 2004 type your answer... 1999 2000 2001 1995 1996 1997 1998 1.474 1.66 1.708 1.787 1.896 1.985 2.035 million dollars

Regression and correlation analysis both describe the strength of linear relationships between variables. Consider the concepts of education and income. Many people would say these two variables are related in a linear fashion. As education increases, income usually increases (although not necessarily at the same rate). Can you think of two variables that are related in such a way that their relationship changes over their range of possible values (i.e., in a curvilinear fashion)? How would you analyze the relationship between two such variables?

Use the time/tip data from the table below, which includes data from New York City taxi rides. (The distances are in miles, the times are in minutes, the fares are in dollars, and the tips are in dollars.) Find the regression equation, letting time be the predictor (x) variable. Find the best predicted tip for a ride that takes 20 minutes. How does the result compare to the actual tip amount of $4.55? Distance 0.68 2.47 12.71 1.80 1.02 8.51 1.32 1.40 6.00 18.00 27.00 25.00 8.00 31.00 8.00 18.00 6.30 14.30 36.80 16.30 7.80 31.75 7.80 12.30 1.89 4.29 0.00 1.50 2.34 2.98 0.00 2.46 Time Fare Tip The regression equation is y=+ X (Round the y-intercept to two decimal places as needed. Round the slope to four decimal places as needed.) The best predicted tip for a ride that takes 20 minutes is $ (Round to the nearest cent as needed.) How does the result compare to the actual tip amount of $4.55? O A. The best predicted value is close to the actual tip of $4.55. O B. The best predicted value…

onsider the following sample of production volumes and total cost data for a manufacturing operation. Production Volume(units) Total Cost($) 400 3,900 450 4,900 550 5,500 600 5,900 700 6,400 750 7,100 This data was used to develop an estimated regression equation,  ŷ = 970.67 + 8.08x,  relating production volume and cost for a particular manufacturing operation. Use  ? = 0.05  to test whether the production volume is significantly related to the total cost. (Use the F test.)   Set up the ANOVA table. (Round your p-value to three decimal places and all other values to two decimal places.) Sourceof Variation Sumof Squares Degreesof Freedom MeanSquare F p-value Regression           Error           Total           Find the value of the test statistic. (Round your answer to two decimal places.)   Find the p-value. (Round your answer to three decimal places.) p-value =  What is your conclusion? Reject H0. We conclude that the relationship between production…