.**.20heightFitted Line Plotheight = 58.89 + 0.9335 age85.00.777951R-Sq92.2%91.6%(pe)bs-82.5O'0877.5O'SL21222425262728agey%3D58.89+0.93xWhere y is measured in cm and x is measured in months.We use this equation to predict the height of a 10 year old and find the child is predicted to be282.09cm tall (this is roughly 9 feet tall).What error occured?We can never make an accurate prediction when we make an estimate.We have extrapolated (looked outside our data range)The correlation coefficient (r) is too low to make an accurate predictionWe can use the equation to find the height of a 10 year old.DEC1.山P!MacBook Pro

Answered: We use this equation to predict the…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Using your favorite statistics software package, you generate a scatter plot with a regression equation and correlation coefficient. The regression equation is reported as . 72.45х + 93.26 y = - 0.23. and the r = What percentage of the variation in y can be explained by the variation in the values of x? r2 = % (Report exact answer, and do not enter the % sign)
A random sample of college students was surveyed about how they spend their time each week. The scatterplot below displays the relationship between the number of hours each student typically works per week at a part- or full-time job and the number of hours of television each student typically watches per week. The correlation between these variables is r = –0.63, and the equation we would use to predict hours spent watching TV based on hours spent working is as follows: Predicted hours spent watching TV = 17.21 – 0.23(hours spent working) Since we are using hours spent working to help us predict hours spent watching TV, we’d call hours spent working a(n) __________________ variable and hours spent watching TV a(n) __________________ variable. The correlation coefficient, along with what we see in the scatterplot, tells us that the relationship between the variables has a direction that is _________________ and a strength that is ______________________. According to…
Please provide a detailed solution for the question below (Table: Physicians per 10,000 Population and Life Expectancy) The table contains a subset of data reported in 2016 by the World Health Organization. Use these data to generate the regression equation for predicting life expectancy in a country from the number of physicians per 10,000 people in that country. To get you started, the mean and the standard deviation for both variables have been calculated. .Please provide detailed solution Table: Physicians per 10,000 Population and Life Expectancy Country Physicians/10,000 people Life expectancy Bangladesh 4.8 72.7 Cambodia 1.7 69.4 United States 25.9 78.5 Egypt 8.1 70.5 France 32.3 82.9 Japan 24.1 84.2
For this project you will collect at least 10 pairs of quantitative data and conduct a hypothesis test for linear correlation and also give the best predicted value of y for a given value of x. Below is my example:(you can't use my example, you have to come up with your own) I am collecting data to see if there is correlation between a person's age and how many hours they use the phone I will also give the best predicted value for a person who is 40 years old. My data age hours 19 7.3 25 6.5 31 4.6 18 3.2 25 5.2 27 2.9 26 3.7 39 6.5 47 4.1 55 3.9 A)l will test the claim that there is no correlation (start with the Claim and finish with the conclusion) C: Ho HA etc B) will give the best estimate for how many hours a person who is 35 years old uses the phone. (make sure to use the write y and x and include the P value in your answer and decide if you will plug in x or find the average of the y's) The P value is (big/small) so I have to (either give the average of the y's or plug in the x…
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The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. x 11.8 8.2 6.8 3.5 2.8 2.8 2.3 0.5 y 13.9 10.8 10.1 7.2 6.5 6.5 6.3 4.8 x = thousands of automatic weapons y = murders per 100,000 residents This data can be modeled by the equation y = 0.81x + 4.35. Use this equation to answer the following; Special Note: I suggest you verify this equation by performing linear regression on your calculator. A) How many murders per 100,000 residents can be expected in a state with 7 thousand automatic weapons? Round to 3 decimal places. B) How many murders per 100,000 residents can be expected in a state with 7.8 thousand automatic weapons? Answer = Answer = Round to 3 decimal places.
Below is a scatterplot of a student’s SAT score and their college GPA. Suppose we want to decide if a student’s SAT score and their GPA are linearly related. What does this scatterplot tell us of the relationship? Support your answer. SAT score_GPA scatterplot.pdf
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You work as a data scientist for a real estate company in a seaside resort town. Your boss has asked you to discover if it's possible to predict how much a home's distance from the water affects its selling price. You are going to collect a random sample of 7 recently sold homes in your town. You will note the distance each home is from the water (denoted by x, in km) and each home's selling price (denoted by y, in hundreds of thousands of dollars). You will also note the product x.y of the distance from the water and selling price for each home. (These products are written in the row labeled "xy"). (a) Click on "Take Sample" to see the results for your random sample. Distance from the water, .x (in km) Take Sample Selling price, y (in hundreds of thousands of dollars) xy Send data to calculator Based on the data from your sample, enter the indicated values in the column on the left below. Round decimal values to three decimal places. When you are done, select "Compute". (In the table…
Find the equation of the regression line for the given data. (The pair of variables have a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The table below shows the heights (in feet) and the number of stories of six notable buildings in a city. What is the regression equation? Predict the value of y for x=503. (Please show me how you figured this out) Predict the value of y for x=641.

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