For data CIR, regress involact on race and interpret the coefficient. Test the hypothesis to determine the claim that homeowners in zip codes with high percent minority are being denied insurance at higher rate than other zip codes. What can regression analysis tell you about the insurance companies claim that the discrepancy is due to greater risks in some zip codes?zip race fire theft age volact involact income
60626 10.0 6.2 29 60.4 5.3 0.0 11744
60640 22.2 9.5 44 76.5 3.1 0.1 9323
60613 19.6 10.5 36 73.5 4.8 1.2 9948
60657 17.3 7.7 37 66.9 5.7 0.5 10656
60614 24.5 8.6 53 81.4 5.9 0.7 9730
60610 54.0 34.1 68 52.6 4.0 0.3 8231
60611 4.9 11.0 75 42.6 7.9 0.0 21480
60625 7.1 6.9 18 78.5 6.9 0.0 11104
60618 5.3 7.3 31 90.1 7.6 0.4 10694
60647 21.5 15.1 25 89.8 3.1 1.1 9631
60622 43.1 29.1 34 82.7 1.3 1.9 7995
60631 1.1 2.2 14 40.2 14.3 0.0 13722
60646 1.0 5.7 11 27.9 12.1 0.0 16250
60656 1.7 2.0 11 7.7 10.9 0.0 13686
60630 1.6 2.5 22 63.8 10.7 0.0 12405
60634 1.5 3.0 17 51.2 13.8 0.0 12198
60641 1.8 5.4 27 85.1 8.9 0.0 11600
60635 1.0 2.2 9 44.4 11.5 0.0 12765
60639 2.5 7.2 29 84.2 8.5 0.2 11084
60651 13.4 15.1 30 89.8 5.2 0.8 10510
60644 59.8 16.5 40 72.7 2.7 0.8 9784
60624 94.4 18.4 32 72.9 1.2 1.8 7342
60612 86.2 36.2 41 63.1 0.8 1.8 6565
60607 50.2 39.7 147 83.0 5.2 0.9 7459
60623 74.2 18.5 22 78.3 1.8 1.9 8014
60608 55.5 23.3 29 79.0 2.1 1.5 8177
60616 62.3 12.2 46 48.0 3.4 0.6 8212
60632 4.4 5.6 23 71.5 8.0 0.3 11230
60609 46.2 21.8 4 73.1 2.6 1.3 8330
60653 99.7 21.6 31 65.0 0.5 0.9 5583
60615 73.5 9.0 39 75.4 2.7 0.4 8564
60638 10.7 3.6 15 20.8 9.1 0.0 12102
60629 1.5 5.0 32 61.8 11.6 0.0 11876
60636 48.8 28.6 27 78.1 4.0 1.4 9742
60621 98.9 17.4 32 68.6 1.7 2.2 7520
60637 90.6 11.3 34 73.4 1.9 0.8 7388
60652 1.4 3.4 17 2.0 12.9 0.0 13842
60620 71.2 11.9 46 57.0 4.8 0.9 11040
60619 94.1 10.5 42 55.9 6.6 0.9 10332
60649 66.1 10.7 43 67.5 3.1 0.4 10908
60617 36.4 10.8 34 58.0 7.8 0.9 11156
60655 1.0 4.8 19 15.2 13.0 0.0 13323
60643 42.5 10.4 25 40.8 10.2 0.5 12960
60628 35.1 15.6 28 57.8 7.5 1.0 11260
60627 47.4 7.0 3 11.4 7.7 0.2 10080
60633 34.0 7.1 23 49.2 11.6 0.3 11428
60645 3.1 4.9 27 46.6 10.9 0.0 13731

For data CIR, regress involact on race and interpret the coefficient. Test the hypothesis to determine the claim that homeowners in zip codes with high percent minority are being denied insurance at higher rate than other zip codes. What can regression analysis tell you about the insurance companies claim that the discrepancy is due to greater risks in some zip codes?zip race fire theft age volact involact income
60626 10.0 6.2 29 60.4 5.3 0.0 11744
60640 22.2 9.5 44 76.5 3.1 0.1 9323
60613 19.6 10.5 36 73.5 4.8 1.2 9948
60657 17.3 7.7 37 66.9 5.7 0.5 10656
60614 24.5 8.6 53 81.4 5.9 0.7 9730
60610 54.0 34.1 68 52.6 4.0 0.3 8231
60611 4.9 11.0 75 42.6 7.9 0.0 21480
60625 7.1 6.9 18 78.5 6.9 0.0 11104
60618 5.3 7.3 31 90.1 7.6 0.4 10694
60647 21.5 15.1 25 89.8 3.1 1.1 9631
60622 43.1 29.1 34 82.7 1.3 1.9 7995
60631 1.1 2.2 14 40.2 14.3 0.0 13722
60646 1.0 5.7 11 27.9 12.1 0.0 16250
60656 1.7 2.0 11 7.7 10.9 0.0 13686
60630 1.6 2.5 22 63.8 10.7 0.0 12405
60634 1.5 3.0 17 51.2 13.8 0.0 12198
60641 1.8 5.4 27 85.1 8.9 0.0 11600
60635 1.0 2.2 9 44.4 11.5 0.0 12765
60639 2.5 7.2 29 84.2 8.5 0.2 11084
60651 13.4 15.1 30 89.8 5.2 0.8 10510
60644 59.8 16.5 40 72.7 2.7 0.8 9784
60624 94.4 18.4 32 72.9 1.2 1.8 7342
60612 86.2 36.2 41 63.1 0.8 1.8 6565
60607 50.2 39.7 147 83.0 5.2 0.9 7459
60623 74.2 18.5 22 78.3 1.8 1.9 8014
60608 55.5 23.3 29 79.0 2.1 1.5 8177
60616 62.3 12.2 46 48.0 3.4 0.6 8212
60632 4.4 5.6 23 71.5 8.0 0.3 11230
60609 46.2 21.8 4 73.1 2.6 1.3 8330
60653 99.7 21.6 31 65.0 0.5 0.9 5583
60615 73.5 9.0 39 75.4 2.7 0.4 8564
60638 10.7 3.6 15 20.8 9.1 0.0 12102
60629 1.5 5.0 32 61.8 11.6 0.0 11876
60636 48.8 28.6 27 78.1 4.0 1.4 9742
60621 98.9 17.4 32 68.6 1.7 2.2 7520
60637 90.6 11.3 34 73.4 1.9 0.8 7388
60652 1.4 3.4 17 2.0 12.9 0.0 13842
60620 71.2 11.9 46 57.0 4.8 0.9 11040
60619 94.1 10.5 42 55.9 6.6 0.9 10332
60649 66.1 10.7 43 67.5 3.1 0.4 10908
60617 36.4 10.8 34 58.0 7.8 0.9 11156
60655 1.0 4.8 19 15.2 13.0 0.0 13323
60643 42.5 10.4 25 40.8 10.2 0.5 12960
60628 35.1 15.6 28 57.8 7.5 1.0 11260
60627 47.4 7.0 3 11.4 7.7 0.2 10080
60633 34.0 7.1 23 49.2 11.6 0.3 11428
60645 3.1 4.9 27 46.6 10.9 0.0 13731

Use the following convention table for R-square.

Upload the ManBody data. Check which of the following four categories (BODYFAT, WEIGHT, HEIGHT, and KNEE) is the most correlated to AGE category. Make a scattered plot chart with X representing the most correlated category and Y representing the AGE, plot the line and compute the R-square. Answer the questions:

I) What category did you choose for X? (10 points)

• a. WEIGHT
• b. BODYFAT
• c. HEIGHT
• d. KNEE

II) Given the plot what is the estimated slope? (10 points)

• a. Between 0 and 0.1
• b. Between 0.1 and 0.3
• c. Between 0.3 and 1.3
• d. Between 1.3 and 10.5
• e. Between 10.5 and 40

III) What does each dot represent? (10 points)

• a. AGE
• b. WEIGHT
• c. HEIGHT
• d. PERSON
• e. BODY FAT

IV) On your chart, there should be a dot that is furthest on the right. What is its approximate X-coordinate? (10 points)

• a. Between 0 and 100
• b. Between 100 and 200
• c. Between 200 and 300
• d. Between 300 and 400

data set:https://www.limes.one/Content/DataFiles/Man_body.txt

Use the following convention table for R-square.

Upload the ManBody data. Check which of the following four categories (BODYFAT, WEIGHT, HEIGHT, and KNEE) is the most correlated to AGE category. Make a scattered plot chart with X representing the most correlated category and Y representing the AGE, plot the line and compute the R-square. Answer the questions:

I) What category did you choose for X? (10 points)

• a. WEIGHT
• b. BODYFAT
• c. HEIGHT
• d. KNEE

II) Given the plot what is the estimated slope? (10 points)

• a. Between 0 and 0.1
• b. Between 0.1 and 0.3
• c. Between 0.3 and 1.3
• d. Between 1.3 and 10.5
• e. Between 10.5 and 40

III) What does each dot represent? (10 points)

• a. AGE
• b. WEIGHT
• c. HEIGHT
• d. PERSON
• e. BODY FAT

IV) On your chart, there should be a dot that is furthest on the right. What is its approximate X-coordinate? (10 points)

• a. Between 0 and 100
• b. Between 100 and 200
• c. Between 200 and 300
• d. Between 300 and 400

data set:https://www.limes.one/Content/DataFiles/Man_body.txt

2. In a survey of 529 travelers, 386 said that location was very important and 323 said that room quality was very important in choosing a hotel.

1. Construct a 90% confidence interval estimate for the population proportion of travelers who said that location was very important for choosing a hotel.
2. Construct a 90% confidence interval estimate for the population proportion of travelers who said that room quality was very important for choosing a hotel.

Need this program in python. The data must be taken from user as input.

Write a program that prompts the user to select either Miles-to-Kilometers or Kilometers-to-Miles, then asks the user to enter the distance they wish to convert. The conversion formula is:

Miles = Kilometers X 0.6214

Kilometers = Miles / 0.6214

Write two functions that each accept a distance as an argument, one that converts from Miles-to-Kilometers and another that converts from Kilometers-to-Miles

The conversion MUST be done as a separate function that is called by the main program.

There are 15 distances in the data (shown below).

Display the original value and its unit (miles/kilometers) and then the converted value and unit.

Data:

Miles 16

Miles 28

Kilometers 39

Kilometers 44

Miles 11

Kilometers 71

Miles 59

Kilometers 62

Kilometers 34

Miles 19

Miles 25

Kilometers 71

Kilometers 88

Kilometers 90

Miles 110

Turn in your program code and the screen shot of the display. Remember you MUST use functions here.

Given the price elasticities and price changes for the following products A–E in the table below, show how much the quantity will change (indicating an increase or decrease) and what effect this will have on total revenue (indicating an increase or decrease). Round your answers to 1 decimal place.

The table below lists weights​ (carats) and prices​ (dollars) of randomly selected diamonds. Find the​ (a) explained​ variation, (b) unexplained​ variation, and​ (c) indicated prediction interval. There is sufficient evidence to support a claim of a linear​ correlation, so it is reasonable to use the regression equation when making predictions. For the prediction​ interval, use a​ 95% confidence level with a diamond that weighs 0.8 carats. Weight 0.3 0.4 0.5 0.5 1.0 0.7 Price ​$517 ​$1163 ​$1350 ​$1410 ​$5672 ​$2278

a. Find the explained variation. nothing ​(Round to the nearest whole number as​ needed.)

b. Find the unexplained variation. nothing ​(Round to the nearest whole number as​ needed.)

c. Find the indicated prediction interval. ​$ nothingless thanyless than​$ nothing ​(Round to the nearest whole number as​ needed.) Enter your answer in each of the answer boxes.

Lifetime Escapes generates average revenue of $7 970 per person on its 7-day package tours to wildlife parks in Zimbabwe. The variable costs per person are as follows:

Annual fixed costs total $400 000.

Required:

1. Calculate the number of package tours that must be sold to break even.
2. Calculate the revenue needed to earn a target profit of $100 000. (1 mark)
3. If fixed costs increase by $19 000, what decrease in variable cost per person must be achieved to maintain the break-even point calculated in requirement 1?
4. The general manager at Lifetime Escapes proposes to increase the price of the package tour to $8500 to decrease the break-even point in units. Using information in the original problem, calculate the new break-even point in units. What factors should the general manager consider before deciding to increase the price of the package tour? (3marks)

question is correct could you please solve ASAP