Look or search for a real-life problem or phenomenon in psychology and consider two important and related variables that have an existing data set. Then perform/ find the following: a.Determine the linear correlation coefficient for the relationship between the two chosen variables. b.Interpret the strength of relationship for the two variables based on the calculated Pearson r value in (a). c.Find the prediction equation of the regression line for the data using the least squares method. d.Construct the scatter plot of the data and the regression line.

Look or search for a real-life problem or phenomenon in psychology and consider two important and related variables that have an existing data set. Then perform/ find the following: a.Determine the linear correlation coefficient for the relationship between the two chosen variables. b.Interpret the strength of relationship for the two variables based on the calculated Pearson r value in (a). c.Find the prediction equation of the regression line for the data using the least squares method. d.Construct the scatter plot of the data and the regression line.

A First Course in Probability (10th Edition)

10th Edition

ISBN:9780134753119

Author:Sheldon Ross

Publisher:Sheldon Ross

Chapter1: Combinatorial Analysis

Section: Chapter Questions

Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...

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The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, y = bo + bjx, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Age 47 49 50 51 58 Bone Density 360 353 336 333 310 Table Copy Data Step 3 of 6: Find the estimated value of y when x = 58. Round your answer to three decimal places.
b. What does the scatter diagram developed in part (a) indicate about the relationship between the two variables? The scatter diagram indicates a positive ✔✔✔ linear relationship between the hotel room rate and the amount spent on entertainment. c. Develop the least squares estimated regression equation. Entertainment = 18.2594 X + 1.0272 Room Rate (to 4 decimals) d. Provide an interpretation for the slope of the estimated regression equation (to 3 decimals). The slope of the estimated regression line is approximately 1.027 So, for every dollar increase ♥ e. The average room rate in Chicago is $128, considerably higher than the U.S. average. Predict the entertainment expense per day for Chicago (to whole number). $ 150 in the hotel room rate the amount spent on entertainment increases by $1.027
An engineer wants to determine how the weight of a gas-powered car, x, affects gas mileage, y. The accompanying data represent the weights of various domestic cars and their miles per gallon in the city for the most recent model year. Complete parts (a) through (d) below. Click here to view the weight and gas mileage data. ..... (a) Find the least-squares regression line treating weight as the explanatory variable and miles per gallon as the response variable. y =x+() (Round the x coefficient to five decimal places as needed. Round the constant to one decimal place as needed.) Car Weight and MPG Miles per Weight (pounds), x Gallon, y 3778 16 3832 16 2778 26 3487 20 3313 22 2896 23 3802 16 2590 24 ar All Check Answer 3441 20 3825 18
Suppose that a kitchen cabinet warehouse company would like to be able to predict the area of a customer’s kitchen using the number of cabinets and the kitchen ceiling height. To do so data is collected on the following variables from a random sample of customers: Area – area of the kitchen in square feet Height – ceiling height in the kitchen (from floor to ceiling) in inches Cabinets – number of cabinets in the kitchen Suppose that a multiple linear regression model was fit to the data and that the following output resulted: Coefficients: (Intercept)HeightCabinets Estimate-57.98771.2760.3393 Std. Error8.63820.26430.1302 t value -6.7134.8282.607 Pr(>|t|)2.75e-074.44e-050.0145 What is the predicted area of a kitchen with a height of 96 inches and 10 cabinets? Report your answer to 1 decimal place. square feet
A researcher collected statistics on the sales amount of a product in 120 different markets and the advertising budgets used in TV, radio and newspaper media channels for each of these markets. The sales amount are expressed in 1000 units, and the budgets are expressed in 1000$. The researcher wants to create a simple linear regression model by choosing one among the TV, radio and newspaper advertising budgets to explain the amount of sales. Accordingly, answer the following question by using the data in the "Regression Data Set" document in the appendix. 1) a) In your opinion, which variable should this researcher choose as an independent variable to the simple regression model? Explain your decision by providing its statistical basis.
An engineer wants to determine how the weight of a gas-powered car, x, affects gas mileage, y. The accompanying data represent the weights of various domestic cars and their miles per gallon in the city for the most recent model year. Complete parts (a) through (d) below. Click here to view the weight and gas mileage data. ..... (a) Find the least-squares regression line treating weight as the explanatory variable and miles per gallon as the response variable. y = + (Round the x coefficient to five decimal places as needed. Round the constant to one decimal place as needed.) (b) Interpret the slope and y-intercept, if appropriate. Choose the correct answer below and fill in any answer boxes in your choice. (Use the answer from part a to find this answer.) O A. For every pound added to the weight of the car, gas mileage in the city will decrease by mile(s) per gallon, on average. It is not appropriate to interpret the y-intercept. B. A weightless car will get miles per gallon, on…
The table below gives the number of hours spent unsupervised each day as well as the overall grade averages for seven randomly selected middle school students. Using this data, consider the equation of the regression line, y = bo + b,x, for predicting the overall grade average for a middle school student based on the number of hours spent unsupervised each day. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Unsupervised 1 1.5 2.5 4 4.5 Overall Grades 97 93 85 74 72 71 66 Table Copy Data Step 3 of 6: Find the estimated value of y when x = 4.5. Round your answer to three decimal places.
For major league baseball teams, do higher player payrolls mean more gate money? Here are data for each of the American League teams in the year 2001 . The variable x denotes the player payroll (in millions of dollars) for the year 2001 , and the variable y denotes the mean attendance (in thousands of fans) for the 81 home games that year. The data are plotted in the Figure 1 scatter plot, as is the least-squares regression line. The equation for this line is =y+13.820.23x . Player payroll, x(in $1,000,000s) Mean attendance, y(in thousands) Anaheim 46.6 24.69 Baltimore 73.4 38.15 Boston 109.6 32.47 Chicago White Sox 62.4 21.85 Cleveland 92.0 39.26 Detroit 49.8 23.70 Kansas City 35.6 19.01 Minnesota 24.4 21.98 New York Yankees 109.8 40.25 Oakland 33.8 26.54 Seattle 75.7 43.33 Tampa Bay 55.0 16.05 Texas 88.5 34.94 Toronto 75.8 23.70 y 5 10 15 20 25 30 35 40 45 x 20 40 60 80 100 120 140 0…
The table below gives the number of hours spent unsupervised each day as well as the overall grade averages for seven randomly selected middle school students. Using this data, consider the equation of the regression line, y = bo + b,x, for predicting the overall grade average for a middle school student based on the number of hours spent unsupervised each day. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Unsupervised 1 1.5 2.5 3.5 5.5 Overall Grades 96 94 89 87 82 74 68 Table Copy Data Step 1 of 6: Find the estimated slope. Round your answer to three decimal places. 田 Tables E Keypad Answer Keyboard Shortcuts How to enter your answer Submit Answer © 2021 Hawkes Learning to search hp
The table below gives the number of hours spent unsupervised each day as well as the overall grade averages for seven randomly selected middle school students. Using this data, consider the equation of the regression line, y = bo + b,x, for predicting the overall grade average for a middle school student based on the number of hours spent unsupervised each day. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Unsupervised 0.5 2 3 4.5 Overall Grades 99 98 96 92 89 88 80 Table Copy Data Step 2 of 6: Find the estimated y-intercept. Round your answer to three decimal places.
To determine the effectiveness of group study sessions, a college instructor gathers data on hours of attendance and exam scores for students in the class. Which variable, hours of attendance or exam scores, would be the response variable for a least-squares regression equation? is it hours of attendance or exam scores?
The data in the table represent the number of licensed drivers in various age groups and the number of fatal accidents within the age group by gender. Complete parts (a) to (c) below. E Click the icon to view the data table. ked Бcor (a) Find the least-squares regression line for males treating the number of licensed drivers as the explanatory variable, x, and the number of fatal crashes, y, as the response variable. Repeat this procedure for females. Find the least-squares regression line for males. Data for licensed drivers by age and gender. (Round the slope to three decimal places and round the constan ion estion 4 Number of Number of tion Number of Male Fatal Licensed Drivers Crashes Number of Female Fatal Licensed Drivers (000s) Crashes Age (000s) (Males) (Females) 74 4,803 2,022 5,375 973 Enter your answer in the edit fields and then click Check Ans Print Done parts remaining