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Can I please have some help with the following, I need to solve theses questions using excel but am not sure about the formulas to

Can I please have some help with the following, I need to solve theses questions using excel but am not sure about the formulas to use so I would just like a little assistance.

evaluate the sale price of each house in Raleigh, NC based on square-footage, number of bedrooms, number of bathrooms and lot size.

Construct a scatterplot for each independent variable (sqft, bed, bath, and lotsize) against the dependent variable (price).

calculate the covariance and correlation coefficient for each scenario

Calculate central and dispersion measurements for each independent variable and the dependent variable (items to include: mean, median, mode, range, MAD, CV, variance, and standard deviation).

Construct 4 SLR models for each independent variable (sqft, bed, bath, and lot size) against the dependent variable (price). Be sure to display the Regression Equation for each model.

Assess goodness of fit based on standard error of measurement, coefficient of determination (R2), and adjusted R2. What is the best model?

Construct 4 MLR models: (1) Sqft and bed against price (2) bath and lot size against price (3) Sqft and bath against price (4) All independent variables (sqft, bed, bath and lot size) verus price (Regression Equation for each model.)

Assess goodness of fit based on standard error of measurement, coefficient of determination (R2), and adjusted R2. What is the best model?

Price SQFT Bed Bath Lot Size
399900 5.026 4 4.5 0.3
375000 3.2 4 3 5
372000 3.22 5 3 5
370000 4.927 4 4 0.3
325000 3.904 3 3 1
325000 2.644 3 2.5 5
319500 5.318 3 2.5 2.5
312900 3.144 4 2.5 0.3
299900 2.8 4 3 5
294900 3.804 4 3.5 0.2
269000 3.312 5 3 1
250000 3.373 5 3.5 0.2
249900 3.46 2 2.5 0.6
244994 3.195 4 2.5 0.2
244900 2.914 3 3 0.3
239900 2.881 4 5 0.3
234900 1.772 3 2 3.6
234000 2.248 3 2.5 0.3
229900 3.12 5 2.5 0.2
219900 2.942 4 2.5 0.2
209900 3.332 4 2.5 0.2
209850 3.407 3 2.5 0.3
206900 2.092 3 2 0.3
200000 3.859 4 2 0.2
194900 3.326 4 2.5 0.1
184900 1.874 3 2 0.5
179900 1.892 3 1.5 0.7
179500 2.5 4 2.5 0.5
165000 2.435 4 2.5 0.4
159900 2.714 3 2.5 0.2
159900 1.85 3 2.5 0.5
155000 3.068 4 3.5 0.2
154900 2.484 4 2.5 0.3
152000 1.529 4 2 0.4
149900 2.876 4 2.5 0.2
148500 2.211 4 2.5 0.1
146900 1.571 3 2 0.2
145500 1.503 4 2 0.5
144900 1.656 3 2 0.5
144900 1.521 3 2 0.6
139900 1.315 3 2 0.2
137900 1.706 3 2 0.3
132900 2.121 4 2.5 0.1
129900 1.306 3 2 0.5
129736 1.402 3 2 0.5
125000 1.325 3 2 0.3
119500 1.234 3 2 0.2
110387 1.292 3 1 0.2
106699 1.36 3 1.5 0.1
102900 1.938 3 1 0.1

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