Consider the following computer output of a multiple regression analysis relating annual salary to years of education and years of work experience. Regression Statistics Multiple R 0.7393 R Square 0.5465 Adjusted R Square 0.5268 Standard Error 2109.9016 Observations 49 ANOVA df SS MS F Significance F Regression 2 246,727,337.6003 123,363,668.8002 27.7117 1.3E-08 Residual 46 204,777,498.3997 4,451,684.7478 Total 48 451,504,836.0000 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 14243.05114 2,495.9107 5.7066 0.000000795 9219.0435 19,267.0588 Education (Years) 2357.5794 333.7454 7.0640 0.000000007 1685.7848 3029.3740 Experience (Years) 830.8237 387.4905 2.1441 0.037342926 50.8458 1610.8016 Step 2 of 2: How much would you expect your salary to increase if you stayed at the company for another year

6 spoons of sugar for every 21 cups of flour

step-by-step explanation:

step-by-step explanation:

step 1: solve one of the equations for one of the variables. let's solve the first equation for y:

step 2: substitute that equation into the other equation, and solve for x.

step 3: substitute x = 4 x = 4 x=4 into one of the original equations, and solve for y.

we have the parent function given by,

1. the function is translated 5 units to the left.

so, we get,   becomes

2. the function is translated 5 units to the right.

so, we get,   becomes

3. the function is translated 5 units upwards.

so, we get,   becomes

4. the function is translated 5 units downwards.

so, we get,   becomes

5. the function is reflected across x-axis.

so, we get,   becomes

6. the function is reflected across y-axis.

so, we get,   becomes .

step-by-step explanation:

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