Calculate the co-efficient of correlation and obtain the least square regression lines for the following data : 3 x: . 1 2 4 5 7 8 9 у: 8 10 12 11 13 14 16 15 Also obtain an estimate of y which should correspond on the average to x = 6.2.
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A: According to the provided information, the regression equation is y=4X+7.
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A: Given, n = 25 r = -0.50 SSX = 38 SSY = 14
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A: We have given that, The data set are :- Hours unsupervised (X) :- 1.5, 2, 3, 4, 5, 5.5, 6 Overall…
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A: The data shows average temperatures and snow accumulations.
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A: Coefficient of determination is denoted by r2
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Q: The table below gives the number of hours spent unsupervised each day as well as the overall grade…
A: here, data given for 7 students. Therefore, n = 7. The slope is estimated as follows:…
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A: Answer:----. Date:----12/10/2021 r = -0.984 So, r^2 = 0.968256
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Q: The table below gives the number of hours spent unsupervised each day as well as the overall grade…
A: The independent variable is Hours Unsupervised. The dependent variable is Overall Grades. This is…
Q: The table below gives the number of hours spent unsupervised each day as well as the overall grade…
A: Here the given information is The table below gives the number of hours spent unsupervised each day…
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Q: The table below gives the number of hours spent unsupervised each day as well as the overall grade…
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Q: E Find the estimated y-intercept. Round your answer to three decimal places.
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A: Answer:---. Date:----30/09/2021
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- Which of the following is not one of the uses of a scatter plot and regression line a. to estimate the average y at a specific value of x. b. All three are uses of the scatterplot and regression line c. to determine if a change in x causes a change in y d. to predict y at a specific value of x.A departmental store has the following sales for a period of last one year of 8 salesmen, who have different years of education. Years of Education 8 10 10 12 14 16 Annual Sales (Thousand Tk.) 100 120 80 100 80 100 70 130 a. Determine the correlation coefficient r between the two variables. Answer rounded to at least 4 decimal places. b. Determine the slope coefficient B1 for the regression of annual sales on years of education. Answer rounded to at least 4 decimal places. c. Determine the y-intercept Bo for the regression of annual sales on years of education. Answer rounded to at least 4 decimal places. d. What will be the annual sales when the years of education is 11 (years)? Answer rounded to at least 4 decimal places. Thousand Tk.The table below gives the age and bone density for 5 women. Use the equation of the regression line, y= b0 + b1x, for predicting a women's bone density based on her age. The correlation coefficient may or may not be statically significant for the data given. Remember it wouldn't be appropiate to use regression line to make a prediction if the correlation coefficient isn;t statically significant. (y has a "hat" on the top) age 39 51 54 56 67 bone density 355 349 347 315 313 Find the estimated slope. Rund your answer to three decimal places. Find the estimated y-intercept. Round your answer to three decimal places. Determine the value of the dependent variable y at x+ 0 (y has a "hat" onthe top) Find the estimated value of y when x = 51. Round your answer to three decimal places. Substitute the values you found in steps 1 and 2 into the equation for the regression line to find the estimated linear model. According to this model, if the valueof the…
- The following data represent the number of flash drives sold per day at a localcomputer shop and their prices.Price Units Sold34 336 432 635 530 938 240 1a. Develop the estimated regression equation that could be used to predict thequantity sold given the price. Interpret the slope.b. Did the estimated regression equation provide a good fit? Explain.c. Compute the sample correlation coefficient between the price and the number offlash drives sold. Use a= 0.01 to test the relationship between price and units sold.d. How many units can be sold per day if the price of flash drive is set to $28.Q3: From the following data: 2 5 y 5 4 6 3 1) Draw scatter plot. 2) Determine the regression line y = a + b x. 3) Predict the value of y for the value of x = 4.6. 4) Calculate the correlation coefficient. 4 8 3 5The table below gives the list price and the number of bids received for five randomly selected items sold through online auctions. Using this data, consider the equation of the regression line, y = bo + b₁x, for predicting the number of bids an item will receive based on the list price. 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. Price in Dollars 23 26 37 41 47 Number of Bids 1 2 4 5 6 Step 1 of 6: Find the estimated slope. Round your answer to three decimal places. Table Copy Data >
- x y A random sample of two variables, x and y produced the following observations: 19 7 13 9 17 8 9 11 12 9 25 6 20 7 3. Test the significance of the slope of the correction model at a = 0.05 17 8 1. Develop a scatter plot of the data. Does the plot suggest a linear or nonlinear relationship betwe dependent and independent variables? 2. Compute the correlation coefficient between x and y (to.05 = 1.9432)There is a relationship between the following variables as y = a + b * (1 / x).Determine the regression coefficients (a and b) of the regression equation.Calculate the correlation coefficient and interpret the degree of correlation by comparing the calculated correlation coefficient with the critical correlation coefficient for 1% significance level, taking into account the number of data. Estimate the y-value for x = 0.8 and the x-value for y = 3.5 x 0,58 0,5 0,32 0,18 0,15 0,1 y 1,2 1,5 2 3 4 5The accompanying data represent the weights of various domestic cars and their gas mileages in the city. The linear correlation coefficient between the weight of a car and its miles per gallon in the city is r= - 0.972. The least-squares regression line treating weight as the explanatory variable and miles per gallon as the response variable is y= - 0.0070x + 44.4405. Complete parts (a) and (b) below. Click the icon to view the data table. ..... (a) What proportion of the variability in miles per gallon is explained by the relation between weight of the car and miles per gallon? The proportion of the variability in miles per gallon explained by the relation between weight of the car and miles per gallon is %. (Round to one decimal place as needed.) (b) Interpret the coefficient of determination. % of the variance in is by the linear model. Data Table (Round to one decimal p Full data set gas mileage Miles per Weight (pounds), x Weight (pounds), x Miles per Gallon, y Car Car Gallon, y…
- The following table gives the data for the average temperature and the snow accumulation in several small towns for a single month. Determine the equation of the regression line, ŷ = bo + b₁x. Round the slope and y-intercept to the nearest thousandth. Then determine if the regression equation is appropriate for making predictions at the 0.05 level of significance. Critical Values of the Pearson Correlation Coefficient Average Temperature (°F) Average Temperatures and Snow Accumulations 42 31 24 45 38 18 33 21 25 9 12 27 7 15 22 30 13 20 37 Snow Accumulation (in.) 811. For temperature (x) and number of ice cream cones sold per hour (y). (65, 8), (70, 10), (75, 11), (80,13), (85, 12), (90, 16). Interpret the coefficient of determination. Optional Answers: 1. 88.2% of the variability in the number of cones sold is explained by the least-squares regression model. 2. 93.9% of the variability in the number of cones sold is explained by the least-squares regression model. 3. 88.2% of the variability in the temperature is explained by the least-squares regression model. 4. 93.9% of the variability in the temperature is explained by the least-squares regression model.The table below gives the number of hours spent unsupervised each day as well as the overall grade averages for five randomly selected middle school students. Using this data, consider the equation of the regression line, yˆ=b0+b1x, 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 2 3 5 6 Overall Grades 90 89 87 77 61 Table Step 2 of 6 : Find the estimated y-intercept. Round your answer to three decimal places.