Suppose that the index model for two Canadian stocks HD and ML is estimated with the following results: RHD =0.02+0.80RM+eHD R-squared =0.6                 RML =-0.03+1.50RM+eML R-squared =0.4 σM =0.20 where M is S&P/TSX Comp Index and RX is the excess return of stock X.  d. For portfolio P with investment proportion of 0.3 in HD and

A The least-squares regression equation is y = 720.8x + 14,490 where y is the median income and x is the percentage of 25 years and older with at least a bachelor's degree in the region. The scatter diagram indicates a linear relation between the two variables with a correlation coefficient of 0.7743. Complete parts (a) through (d). (a) Predict the median income of a region in which 20% of adults 25 years and older have at least a bachelor's degree. (Round to the nearest dollar as needed.) G Median Income 55000- 20000+ 15 20 25 30 35 40 45 50 55 60 Bachelor's % Q

(a) Run linear regression on the closing price data for BTC in the range of 12/15/2020 to12/28/2020. Report the r-squared value on your training data. Use this to predict BTCclosing prices on 1/1/2021 and 1/8/2021. How close are your predictions to the actual?Repeat with polynomial regression.(b) Run linear regression on the closing price data for BTC in the range of 2/2/2021 to 2/15/2021.Report the r-squared value on your training data. Use this to predict BTC closing priceson 2/19/2021 and 2/21/2021. How close are your predictions to the actual? Repeat withpolynomial regression.(c) What is the daily rate of change on average of BTC closing prices based on your predictionsobtained from regression on the data in the range of 12/15/2020 to 12/28/2020. What aboutfor 2/2/2021 to 2/15/2021?

Consider the following estimated regression model relating annual salary to years of education and work experience. Estimated Salary=11,656.59+2985.74(Education)+1180.42(Experience)Estimated Salary=11,656.59+2985.74(Education)+1180.42(Experience) Suppose two employees at the company have been working there for five years. One has a bachelor's degree (88 years of education) and one has a master's degree (1010 years of education). How much more money would we expect the employee with a master's degree to make

The data shown below are the initial weights and gains in weight (in grams) of female rats on a high protein diet from 24 to 84 days of age. The point of interest in these data is whether the gain in weight is related to (dependent on) the initial weight. If so, then feeding experiments on female rats can be made more precise by adjusting for differences in initial weights of the rats. Statistically test this with a regression analysis.   Rat Number Initial Weight (gms) Weight Gain (gms) 1 50 128 2 64 159 3 76 158 4 64 119 5 74 133 6 60 112 7 69 96 8 68 126 9 56 132 10 48 118 11 57 107 12 59 106 13 46 82 14 45 103 15 65 104 I used excel's regression took pak and got the following:  SUMMARY OUTPUT                                 Regression Statistics               Multiple R 0.489416452               R Square 0.239528464               Adjusted R Square 0.181030653               Standard Error 8.869929833               Observations 15…

The least-squares regression equation is y = 689.9x + 14,803 where y is the median income and x is the percentage of 25 years and older with at least a bachelor's degree in the region. The scatter diagram indicates a linear relation between the two variables with a correlation coefficient of 0.7256. Complete parts (a) through (d). (a) Predict the median income of a region in which 20% of adults 25 years and older have at least a bachelor's degree. (Round to the nearest dollar as needed.) . TOLED dian Income Media 55000- 20000- 15 20 25 30 35 40 45 50 55 60 Bachelor's 96 Q

The​ least-squares regression equation is y=761.7x+13,208 where y is the median income and x is the percentage of 25 years and older with at least a​ bachelor's degree in the region. The scatter diagram indicates a linear relation between the two variables with a correlation coefficient of 0.7483.   Predict the median income of a region in which 20​% of adults 25 years and older have at least a​ bachelor's degree.

Contains data on brain mass in different species versus glia-neuron ratio, the latter being a measurement of brain metabolism as the glia provides the metabolic needs of the neurons.  The relationship between THE LOGARITHM of the brain mass (in the third column) and Glia-neuron ratio (fourth column) appears linear and it is these two variables that we wish to analyze via linear regression.  We would like to know if the human brain fits the trend from the other species.  Towards this end we will perform the regression on all species EXCEPT humans (Homo sapiens).  Again, throw out the human data from your analysis.  You will however need the human numbers for some of the questions.  The analysis to be performed is as follows: 1. Calculate the regression line (slope and intercept) 2. Perform an ANOVA test of the null hypothesis for zero slope.  From this analysis, obtain SStotal, SSregression and SSresidual as well as the corresponding MS statistics. 3. Perform a t-test of the null…

The​ least-squares regression equation is y=784.6x+12,431 where y is the median income and x is the percentage of 25 years and older with at least a​ bachelor's degree in the region. The scatter diagram indicates a linear relation between the two variables with a correlation coefficient of 0.7962. Predict the median income of a region in which 25​% of adults 25 years and older have at least a​ bachelor's degree.

We want to understand the relationship between sales growth rates (s) and project FCF growth rates (g). This is useful to think about, because in our spreadsheet models we often need an estimate of g but when we look for analyst forecasts, they often just apply to s. We consider a hypothetical firm in which there are Sales, COGS, Depreciation, Taxes, ONWC, and CAPEX occurring each year. You know that sales growth is constant and equals s. You further know that in any given year, COGS/Sales-c, the tax rate is p, the ratio of ONWC to Sales is n, and CAPEX-Depreciation, and Depreciation/Sales-d. s, c, p, n, and d are constants (i.e., fixed parameters that don't change over time). Perform an algebraic series of calculations. Start with FCF = (Sales - COGS - DA - Taxes - etc). Simplify the expression until it is in the form Sales. *((1-c-d)(1-p)-s/(1+s)*n).