A linear model was fit to predict weekly Sales of frozen pizza (in pounds) from the average Price (S/unit) charged by a sample of stores in a city in 39 weeks over a three-year period. The average Price of the pizza was $3.44 (SD = $0.34), and the average Sales were 59,981 pounds (SD = 10,719 pounds). If the Price in a particular week was equal to the mean price of $3.44, how much pizza would you predict was sold that week? Answer to the nearest pound.

Example 5 The highlights of the Railway budget for 1983-84 are as follows. The actual gross traffic receipts for 1981-82 was Rs. 3,538.24 crores. The Budget Estimates for 1982-83 in regard to Gross traffic receipts was Rs. 4,171.8 crores and the revised Budget Estimate for 1982-83 for the above item is Rs. 4,375.79 crores respectively. Whereas the budget estimates for 1983-84 for Gross traffic receipts is Rs. 5,343.63 crores, the actual total working expenses and the net railway revenue for 1981-82 were Rs. 3,182.05 crores and Rs. 403.1 crores respectively. The Budget Estimates and Revised Budget Estimates for 1982-83 in respect of total working expenses are Rs. 3,700.90 crores and Rs. 3,892.28 crores respectively. Whereas the corresponding estimates for 1982-83 in respect of net-railway revenue are Rs. 510.91 crores and Rs. 533.4 crores respectively, the estimated budget on total working expenses and net railway revenue for 1983-84 exceed the actuals for 1981-82 by Rs. 1,338.72 crores…

The electric power consumed each month by a chemical plant is thought to be related to the average ambient temperature x1, the number of days in the month x2, the average product purity x3, and the tons of product produced x4. The past year's historical data are available and are presented in the following table. y 240 236 290 274 214 301 316 910 300 300 296 x1 25 31 45 60 00 65 99 72 14 X₂ 24 21 24 25 20 25 20 26 20 25 20 80 90 84 267 75 276 60 288 50 261 38 23 25 24 24 25 25 X3 91 90 88 00 87 91 94 2 87 DA 86 20 88 91 90 89 X₁ 100 95 110 88 94 99 97 96 110 105 100 98 Predict power consumption for a month in which x₁ =75°F, x₂ =24 days, x3 =90%, andx4 = 98 tons. Use 1 decimal place.

Suppose an oceanographer monitors the daily salinity of a particular ocean in relation to the temperature of the water. The oceanographer plots the data with temperature, in degrees Celsius (°C), along the horizontal axis and salinity, in parts per thousand (ppt), along the vertical axis.   Select the true statement about the data point identified by the arrow. An ocean temperature of 21 °C causes the salinity to be 29 ppt. The predicted salinity is 29 ppt when the ocean temperature for the day is 21 °C. An ocean temperature of 29 °C corresponds to a salinity level of 21 ppt. The observed salinity is 29 ppt when the ocean temperature for the day is 21 °C. There is no relationship between the temperature of the ocean on a given day and salinity.

The amount of time adults spend watching television is closely monitored by firms because this helps to determine advertising pricing for commercials. Complete parts​ (a) through​ (d). ​(a) Do you think the variable​ "weekly time spent watching​ television" would be normally​ distributed? If​ not, what shape would you expect the variable to​ have?     A. The variable​ "weekly time spent watching​ television" is likely skewed​ left, not normally distributed.   B. The variable​ "weekly time spent watching​ television" is likely normally distributed.   C. The variable​ "weekly time spent watching​ television" is likely​ uniform, not normally distributed.   D. The variable​ "weekly time spent watching​ television" is likely​ symmetric, but not normally distributed.   E. The variable​ "weekly time spent watching​ television" is likely skewed​ right, not normally distributed.

An economist conducted a study of the possible association between weekly income and weekly grocery expenditures. The particular interest was whether higher income would result in shoppers spending more on groceries. A random sample of shoppers at a local supermarket was obtained, and a questionnaire was administered asking about the weekly income of the shopper’s family and the grocery bill for that week. The correlation was found to be r=0.675. The percentage (to two decimal places) of variation in grocery bills explained by the family's weekly income regression line is:

We want to predict the selling price of a house in Newburg Park, Florida, based on the distance the house lies from the beach. Suppose that we're given the data in the table below. These data detail the distance from the beach (x, in miles) and the selling price (y, in thousands of dollars) for each of a sample of sixteen homes sold in Newburg Park in the past year. The data are plotted in the scatter plot in Figure 1. Also given is the product of the distance from the beach and the house price for each of the sixteen houses. (These products, written in the column labelled "xy", may aid in calculations.) Distance from the beach, x (in miles) 7.7 18.5 6.3 11.9 11.0 14.6 11.3 3.0 Selling price, y (in thousands of dollars) 290.8 215.0 304.4 269.9 283.2 203.4 212.4 263.2 197.3 272.2 215.1 241.6 312.3 221.5 279.1 236.1 10.9 4.9 12.5 5.8 3.6 5.7 9.4 8.1 Send data to calculator xy 2239.16 3977.5 1917.72 3211.81 3115.2 2969.64 2400.12 789.6 2150.57 1333.78 2688.75 1401.28 1124.28 1262.55…

In industry, the relationship between wages and the quit ratio of employees is defined to be the percentage of employees that quit within 1 year of employment. The quit ratio of a large restaurant chain that paid its employees the minimum hourly wage ($7.25 per hour) was .2 or 20 employees per 100. When the company raised the hourly wage to $8, the quit ratio dropped to .18, or 18 employees per 100. (a) Assuming a linear relationship between the quit ratio Q(x) and the hourly wage x, find an expression for Q(x). (b) What should the hourly wage be for the quit ratio to drop to 10 employees per 100?

We want to predict the selling price of a house in Newburg Park, Florida, based on the distance the house lies from the beach. Suppose that we're given the data in the table below. These data detail the distance from the beach (x, in miles) and the selling price (y, in thousands of dollars) for each of a sample of fifteen homes sold in Newburg Park in the past year. The data are plotted in the scatter plot in Figure 1. Also given is the product of the distance from the beach and the house price for each of the fifteen houses. (These products, written in the column labelled "xy", may aid in calculations.) Distance from the beach,  x (in miles) Selling price,  y (in thousands of dollars) xy 5.0 270.1 1350.5 11.5 205.9 2367.85 5.9 309.4 1825.46 12.2 200.6 2447.32 2.6 307.1 798.46 5.9 266.0 1569.4 8.3 297.3 2467.59 18.3 224.2 4102.86 6.5 242.4 1575.6 12.1 192.6 2330.46 11.6 229.4 2661.04 9.5 230.9 2193.55 10.1 277.0 2797.7 13.5 270.8 3655.8 6.2…

From the data given below, using the simple average of price relative comput: (i) Index number for 1998 with 1995 as the base and (ii) Index number for 1995 with 1998 as the base. Comment upon the result, if any. Price Items Unit 1995 1998 6. Rice Dal Ghee kg kg kg 15 20 40 50