a) In a study of the relationship between X-mean daily temperature for the month and Y=monthly charges on electrical bill, the following data was gathered: X Y 20 125 30 110 50 95 60 85 80 75 у on X (i) Find the equation of regression line of monthly charges on mean daily temperature using the least squares method. (ii) Interpret the values of estimated slope and intercept. (iii) Estimate the predicted monthly charges on electricity bill if the temperature is 40.
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- 2. The table below lists the annual land-line phone cost per costumer: Year 2012 2013 2014 2015 Cost ($) a. 692 610 Find a linear regression model for this data b. Interpret the slope of the model 580 C. Predict the annual land-line phone cost per customer in 2022 495 2016 434A chemistry experiment is performed measuring the solubility of potassium chloride (KCl) in water at different temperatures. The goal was to determine if there is a linear relationship between the temperature of the water and how much KCl can dissolve, measured as grams per 100 milliliter (g/100mL). After the experiments were performed, the following data was collected with temperature being the independent x-variable and solubility being the dependent y-variable: Temperature (°C) x Solubility (g/100mL) y 10 31 20 33 30 37 40 41 50 42 Based on the data given for temperature and solubility of KCl and without doing any math yet, which of the following do you predict would best describe the relationship between these variables? A positive linear relationship (r close to 1) A positive linear relationship (r close to -1) A negative linear relationship (r close to 1) A negative linear relationship (r close to -1)…You are given the following data, where X1 (final percentage in math class) and X2 (number of absences) are used to predict Y (standardized math test score in third grade): Y X1 X2 345 70 390 80 1 370 75 4 375 92 400 82 350 70 3 310 61 5 420 80 375 88 3 410 72 1 485 99 300 65 7 Determine the following multiple regression values. Report intercept and slopes for regression equation accurate to 3 decimal places: Intercept: a = Partial slope X1: bị = Partial slope X2: bz = Report sum of squares accurate to 3 decimal places: SSreg Test the significance of the overall regression model (report F-ratio accurate to 3 decimal places and P-value accurate to 4 decimal places): F-ratio = P-value = Report the variance of the residuals accurate to 3 decimal places: Sres = Report the results for the hypothesis test for the significance of the partial slope for final percentage in math class (report the test statistic for the regression coefficients accurate to 3 decimal places and P-value accurate to…
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- A linear relationship between EmployeeSalary (Dependent) and degree(independent) has the following equation : Salary = 400+0.2 (Degree). SST= 736, SSR= 385. Calculate and interpret the coefficient of determination (r2) : Select one: O a. 0.48 , 47.69 percent of the variability in employee salary can be explained by the simple linear regression equation Ob. 0.52,52.31 percent of the variability in employee salary can be explained by the simple linear regression equation Oc. 0.48, 47.69 percent of the variability in the degree earned can be explained by the simple linear regression equation F Od. 0.52, 52.31 percent of the variability in the degree earned can be explained by the simple linear regression equation Next page JUN 2 12 étv W Ps Lr9 students were surveyed to see what their age is and what their income level is. Find the equation of the line using linear regression. We want to predict their age using their income. age 18 24 38 22 19 35 28 19 27 income 456 786 835 855 645 244 587 1400 975 Just a side note, the 19 year old is making 1400, would be considered an outlier since they are making way more than everyone else. (y=-.0061x+34.9874 4 decimals) y=30.3709-.0064Range of ankle motion is a contributing factor to falls among the elderly. Suppose a team of researchers is studying how compression hosiery, typical shoes, and medical shoes affect range of ankle motion. In particular, note the variables Barefoot and Footwear2. Barefoot represents a subject's range of ankle motion (in degrees) while barefoot, and Footwear2 represents their range of ankle motion (in degrees) while wearing medical shoes. Use this data and your preferred software to calculate the equation of the least-squares linear regression line to predict a subject's range of ankle motion while wearing medical shoes, ?̂ , based on their range of ankle motion while barefoot, ? . Round your coefficients to two decimal places of precision. ?̂ = A physical therapist determines that her patient Jan has a range of ankle motion of 7.26°7.26° while barefoot. Predict Jan's range of ankle motion while wearing medical shoes, ?̂ . Round your answer to two decimal places. ?̂ = Suppose Jan's…
- In linear regression analysis, the coefficient for the x-variable when the y-variable is regressed on the x-variable can be thought of as: Note: more than one answer may be correct Group of answer choices How much the value of the predicted y-variable will change when the x-variable changes by one unit. In the simple (two variable) linear regression model, the coefficient can be thought as the slope coefficient that measures the responsiveness of y to changes in x. The estimated coefficient will change if the sample containing x and y changes. The sample coefficient is an estimate of the population coefficient Before interpreting the coefficient for the x-variable, we should test whether the coefficient is statistically significant.The following table contains ACT scores and the GPA for eight college students. Estimate the relationship between GPA (yi) and ACT (x;) using OLS regression by hand. Report the intercept and slope estimates: Student 1 2 3 4 5 6 7 8 GPA 2.8 3.4 3.0 3.5 3.6 3.0 2.7 3.7 ŷ₁ =B₁ + B₁x₁ ACT 21 24 26 27 29 25 25 30 y X ŷ₁A financial website reported the beta value for a certain company was 0.86. Betas for individual stocks are determined by simple linear regression. The dependent variable is the total return for the stock, and the independent variable is the total return for the stock market, such as the return of a market index. The slope of this regression equation is referred to as the stock's beta. Many financial analysts prefer to measure the risk of a stock by computing the stock's beta value. Suppose the following data show the monthly percentage returns for the market index and the company for a recent year. Month Market Index% Return Company% Return August -3 4 September 8 7 October 0 1 November -2 1 December -5 0 January 0 0 February 7 7 March 0 -2 April 2 0 May -5 -1 a. Develop the least squares estimated regression equation. (Let x = Market Index % Return (as a %), and let y = Company % Return (as a %). Round your numerical values to four decimal places.)