Regression analysis :Least squares methods
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An electric utility wishes to examine the relationship between temperature and electricity use in its service region during the summer months. The bivariate data below give the maximum temperature (denoted by x, in degrees Fahrenheit) and the electricity use (denoted by ,y in thousands of kilowatt hours) for a random sample of fifteen summer days. The data are shown in the Figure 1 scatter plot. Also given are the products of the temperature values and values for electricity use for each of the fifteen days. (These products, written in the column labelled "x,y" may aid in calculations.) .
x y xy
92.4 342.3 31628.52
69.1 306.8 21199.88
89 313.1 27865.9
82.5 311.3 25682.25
70.7 282.7 19986.89
84.6 353 29368.8
82.3 258.4 21266.32
94.8 334.8 31739.04
72.4 231.3 16746.12
82.2 322.3 26493.06
99.1 381.6 37816.56
90 312.1 28089
75.3 293.7 22115.61
96.6 310.2 29965.32
93.9 371 34836.9
What is the value of the slope of the least-squares regression for these data rounded to two decimal points?
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Step by step method for computing regression model under least squares method.
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Regression
An electric utility wishes to examine the relationship between temperature and electricity use in its service region during the summer months. The bivariate data below give the maximum temperature (denoted by x, in degrees Fahrenheit) and the electricity use (denoted by ,y in thousands of kilowatt hours) for a random sample of fifteen summer days. The data are shown in the Figure 1 scatter plot. Also given are the products of the temperature values and values for electricity use for each of the fifteen days. (These products, written in the column labelled "x,y" may aid in calculations.) .
x y xy
92.4 342.3 31628.52
69.1 306.8 21199.88
89 ...
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