Café Michigan's manager, Gary Stark, suspects that demand for mocha latte coffees depends on the price being charged. Based on historical observations, Gary has gathered the following data, which show the numbers of these coffees sold over six different price values: Price $2.70 $3.50 Number Sold 760 510 980 250 320 480 $2.00 $4.20 $3.10 $4.05 Using simple linear regression and given that the price per cup is $2.80, the forecasted demand for mocha latte coffees will be cups (enter your response rounded to one decimal place).

The table shows the national expenditures for health care in billions, in a country for the selected years from 1990 to 2020.  Given the projection cost for the year 2002, identify the type of regression point.   Year                      Exprenditures ($billions) 1991                        1020 1994                         1356 1998                         1984 2004                          2984 2009                           3374 2016                           4765 2020                            5937   Interpolation Extrapolation Neither Both

2.  Following data is amount of Assessed Value for an office building for a given floor area, number of office space, number of entrances, and the age of the offic building.   Use excel to ….   Obtain a Scatter plot of Floor Area as the independent and Assessed value as the dependent variable.  Note the linear regression Model  Predict the assessed value for 6000 sq ft.      FloorArea (Sq.Ft.) Offices Entrances Age AssessedValue ($'000) 4790 4 2 8 1796 4720 3 2 12 1544 5940 4 2 2 2094 5720 4 2 34 1968 3660 3 2 38 1567 5000 4 2 31 1878 2990 2 1 19 949 2610 2 1 48 910 5650 4 2 42 1774 3570 2 1 4 1187 2930 3 2 15 1113 1280 2 1 31 671 4880 3 2 42 1678 1620 1 2 35 710 1820 2 1 17 678 4530 2 2 5 1585 2570 2 1 13 842 4690 2 2 45 1539 1280 1 1 45 433…

5. The table below shows varying prices of music sold on a streaming service. Determine the linear regression to estimate the weekly sales of albums sold, use your regression to estimate the weekly sales if the price is $9 per album, and state the correlation coefficient, r, and interpret its meaning in the context of this problem. Price ($) Weekly sales (in thousands) $8 440 $8.50 380 $10 $11.50 250 180

Management at Jagoda Wholesalers, in Calgary, Canada, has used time-series regression based on point-of-sale data to forecast sales for the next 4 quarters. Sales estimates are $100,000, $120,000, $140,000, and $160,000 for the respective quarters. Seasonal indices for the four quarters have been found to be1.30, .90, .70, and 1.10, respectively.

The following chart shows the actual sales for the last 12 months for a given company. Assume that sales are best fit by a linear trend and you can use single linear regression to set up a forecasting model. Using the sales data answer below questions (justify your answers):   A.What would be the typical linear regression equation for the number of sales?    B.Make the sales forecast for period 15 of next year.    C. Make the sales forecast for period 17 of next year.      D. What is the standard error for the data?

15. Listed below are paired data consisting of movie budget amounts and the amounts that the movies grossed. Find the regression equation, letting the budget be the predictor (x) variable. Find the best predicted amount that a movie will gross if its budget is $115 million. Use a significance level of a = 0.05. Budget ($)in Millions Gross ($) in Millions %3D 39 23 117 70 70 49 122 62 64 122 20 11 149 111 18 104 68 129 116 98 95 60 113 208 40 22 288 48 8. Click the icon to view the critical values of the Pearson correlation coefficient r. The regression equation is y = (Round to one decimal place as needed.) х. The best predicted gross for a movie with a $115 million budget is $ million. (Round to one decimal place as needed.)

$339,380 $41,768 A national homebuilder builds single-family homes and condominium-style townhouses. The accompanying dataset provides information on the selling price, lot cost, and type of home for closings during one month. Complete parts a through c. E Click the icon to view the house sales data a. Develop a multiple regression model for sales price as a function of lot cost and type of home without any interaction term. Create a dummy variable named "Townhouse", where it is equal to 1 when Type = "Townhouse" and 0 otherwise. Determine the coefficients of the regression equation. Sales Price = 108726 + (3.68 ) • Lot Cost + ( - 75063 )• Townhouse (Round the constant and coefficient of Townhouse to the nearest integer as needed. Round all other values to two decimal places as needed.) b. Determine if an interaction exists between lot cost and type of home and find the best model. Use a = 0.1 as the level of significance. First determine whether an interaction exists. Select the…

Suppose a firm has had the following historic sales figures. Year: 2016 2017 2018 2019 2020 Sales $2,570,000 $3,750,000 $4,380,000 $5,010,000 $5,520,000 What would be the forecast for next year’s sales using regression to estimate a trend?

The following table shows retail sales in drug stores in billions of dollars in the U.S. for years since 1995. Year Retail Sales 0 85.851 3 108.426 6 141.781 9 169.256 12 202.297 15 222.266 Let � be the retails sales in billions of dollars in � years since 1995. A linear model for the data is �=9.44�+84.182.36912158090100110120130140150160170180190200210220A) Use the above scatter plot to decide whether the line of best fit, fits the data well. The function is a good model for the data. The function is not a good model for the data B) To the nearest billion, estimate the retails sales in the U. S. in 2017.  billions of dollars.C) Use the equation to find the year in which retails sales will be $231 billion.