Questionsheila would like to sell her 2017 honda civic, and she needs to… Page 3 of 6 ZOOM + T S 5a Scatterplot Data $24,000.00 $22,000.00 $20,000.00 $18,000.00 $16,000.00$14,000.00 Y Mean (9779.5) $10,000.00 $8,000.00 $6,000.00 $4,000.00 $2,000.00 $- 1 2 3 4 5 1 5 6 7 8 9 1011 12 13 14 15 16 17 18 19 20 Age of Car (X) 5b The direction of the data is a downward trend. form… Show more… Show moresheila would like to sell her 2017 honda civic, and she needs to know what price to ask for it in order to write an ad. the car is in fair condition, and sheila expects to get an average price for it. the question she must answer is “what is an expected selling price for a 2017 honda civic?” a one-day inspection of the classified section of her local newspaper turned up only two ads for 2017 honda civics, and the two prices listed varied a great deal. sheila’s statistician husband convinced her that, in order to model accurately the selling behavior for civics, she should define two variables – age and price – and collect several pairs of values from want ads by individuals. we used the following definitions: x = age of the car in years (current year minus year of manufacture), and y = the advertised asking price.for this project, you must collect your own data from classified ads (kbb values are not allowed). collect ads which match the brand/make of the car that you pick as closely as possible (but a range of different years, of course). try to find at least two ads for each year and then summarize them using the average of the advertised prices as the y coordinate. try, as much as is reasonably possible, to select an equal number of ads from both the older side and the younger side of the year you’re targeting. (if you are unable to do this, then that might be a source of error that you’d want to discuss in your write-up later on.) do not use any ads that would obviously be special outliers, such as cars that have very rare and expensive features or “classic” models that don’t fit the usual deterioration).you may substitute a different make, model, and year of car for this problem (for example, 2017 mustang); the rest of the instructions and questions will remain the same. one caution: don’t pick a model year that’s so extreme that you’re unable to find data on either side of it! for example, this model year’s corvette wouldn’t work.) when collecting data, make sure the same ad is not used twice because this would introduce bias into your data.tasks1. collect a sample of at least n = 20 pieces of bivariate (two variables) data – for example, the ordered pair (6, 9600) would represent a car that’s six years old and sells for $9600. a.list your data with a column for the year, price of first car, price of second car, average price, ordered pair (age, average price).b. calculate the descriptive statistics for each variable: the five-number summary, mean and standard deviation.c.analyze the measures of center and spread of the two sets of data.2. create an appropriate graphical display for the price data. be sure to include scales, labels, etc.3. describe how you collected your data and how you tried to make the collection of data fair and unbiased. do not go overboard in complexity, but try to gather your data in a way that yields a good representation. list the websites or attach the ads that you used.4. a. identify the explanatory (treatment) variable and the response variable. b. do you think these two variables are positively or negatively associated? strong of a relationship do you think this is?5. a. make a scatterplot of your data. be sure to properly label the axes. b. comment on the form and direction of the data.6. a. give the correlation coefficient r for these data and describe the strength.b. are the strength and sign (direction) of r what you expected before the data were obtained? why or why not?c. does r seem to be a good indicator of the strength of the relationship between your two variables? (in other words, is r useful?) why or why not?7. a. graph the line on your scatterplot.y=yb. is this line a good model for the data? why or why not?8. calculate and interpret the coefficient of determination, r.9. a. calculate the equation of the least-squares regression line.b. draw this line on your scatterplot.c. the point ( ) should lie on the line. show, using algebra, that this point does indeed lie x,yon the line.d. plot and label this point on the least-squares regression line.10. calculate all residuals and present this information in a table or upload a picture of all residuals from your calculator.11. a. calculate and interpret the value of the slope in your equation.b. do you think this is a fixed value for automobile selling prices or not? explain.12. a. calculate and interpret the value of the y intercept in your equation.b. is this value realistic?13. a. use your regression equation to predict the asking price for sheila’s 2017 civic (or the car/year that you targeted.)b. how does this predicted value compare to the , average selling price of the yy data (not this particular year)?c. which of these two values is likely to be more accurate? why?14. which of the two models, , if either, is best at representing the data you y=yorlsrlcollected? make any final comments about the effectiveness of this procedure, any flaws you see in your data collection or analyses, and any other matter that you consider relevantMathStatistics and Probability STA 2023

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