A firm purchases two types of industrial chemicals. Type I chemical costs $3 per gallon, whereas type II costs $5 per gallon. The mean and variance for the number of gallons of type I chemical purchased, Y, are 40 and 4, respectively. The amount of type II chemical purchased, Y2, has E(Y2) = 65 gallons and V(Y2) = 8. Assume that Y, and Y2 are independent and find the mean and variance of the total amount of money spent per week on the two chemicals.

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…

The weights (in pounds) of 6 vehicles and the variability of their braking distances (in feet) when stopping on a dry surface are shown in the table. Can you conclude that there is a significant linear correlation between vehicle weight and variability in braking distance on a dry surface? Use a = 0.05. Weight, x Variability in braking distance, y 5980 5370 6500 5100 5820 4800 1.72 1.97 1.87 1.63 1.64 1.50 Click here to view a table of critical values for Student's t-distribution. Setup the hypothesis for the test. Ho: Ha: P Identify the critical value(s). Select the correct choice below and fill in any answer boxes within your choice. (Round to three decimal places as needed.) O A. The critical value is. B. The critical values are - to and to %3D Calculate the test statistic. t= (Round to three decimal places as needed.) What is your conclusion? There enough evidence at the 5% level of significance to conclude that there a significant linear correlation between vehicle weight and…

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…

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). C (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 right, not normally distributed. OB. The variable "weekly time spent watching television" is likely symmetric, but not normally distributed. O C. The variable "weekly time spent watching television" is likely skewed left, not normally distributed. O D. The variable "weekly time spent watching television" is likely normally distributed. O E. The variable "weekly time spent watching television" likely uniform, not normally distributed. (b) According to a certain survey, adults spend 2.45 hours per day watching television on a weekday. Assume that the standard deviation for "time…

The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. xx 11.8 8.4 7.2 3.6 2.7 2.7 2.2 0.7 yy 13.8 11.5 10 7.2 6.4 6.1 6.2 4.4 xx = thousands of automatic weaponsyy = murders per 100,000 residents This data can be modeled by the equation y=0.84x+4.06.y=0.84x+4.06. Use this equation to answer the following;A) How many murders per 100,000 residents can be expected in a state with 2.7 thousand automatic weapons?Answer =  Round to 3 decimal places.B) How many murders per 100,000 residents can be expected in a state with 4.9 thousand automatic weapons?Answer =  Round to 3 decimal places.

A traffic safety company publishes reports about motorcycle fatalities and helmet use. In the first accompanying data table, the distribution shows the proportion of fatalities by location of injury for motorcycle accidents. The second data table shows the location of injury and fatalities for 2061 riders not wearing a helmet. Complete parts (a) and (b) below. Click the icon to view the tables. (a) Does the distribution of fatal injuries for riders not wearing a helmet follow the distribution for all riders? Use α = 0.05 level of significance. What are the null and alternative hypotheses? O A. Ho: The distribution of fatal injuries for riders not wearing a helmet follows the same distribution for all other riders. H₁: The distribution of fatal injuries for riders not wearing a helmet does not follow the same distribution for all other riders. B. Ho: The distribution of fatal injuries for riders not wearing a helmet does not follow the same distribution for all other riders. H₁: The…

1945 is paired with cancer rates for 1975. The cigarette consumption data is from 1945 to 1975 in 5-year increments. There is a strong positive linear association between annual cigarette consumption and lung cancer rates. The correlation is 0.81. If we use the line to make predictions of the number of lung cancer deaths per 100,000 people, which of the following is an example of extrapolation? a. Predict Y when cigarette consumption is 3,500 cigarettes per person. b. Predict Y when cigarette consumption is 3,800 cigarettes per person. c. Predict Y when cigarette consumption is 4,600 cigarettes per person.

An office supply company services copiers and tracks how many machines are serviced and the length of time (in minutes) for a service call. The data below for 11 clients is below.   Sum X = 46   Sum Y= 234  Sum XY = 5797       Sum X2 =1180         Sum Y2 =146608     1) Calculate SSYY, SSXX, SSXY   2) Calculate b0, b1   3) Interpret the estimated slope coefficient.

The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. 11.6 8.5 7.1 3.4 2.6 2.8 2.6 0.7 y 13.8 11.3 9.9 6.8 6.4 6.7 6.1 4.4 x = thousands of automatic weapons y = murders per 100,000 residents This data can be modeled by the equation y = 0.85x +4.02. Use this equation to answer the following; Special Note: I suggest you verify this equation by performing linear regression on your calculator. Use the equation with the values rounded to two decimal places to make your predictions. A) How many murders per 100,000 residents can be expected in a state with 6.7 thousand automatic weapons? Round to 3 decimal places. B) How many murders per 100,000 residents can be expected in a state with 8 thousand automatic weapons? Answer= Answer = Round to 3 decimal places.