Explain correlation coefficient between rain and yield

Reference no: EM131466329

Modelling Yield

Molineux Vineyards is interested in examining various factors relating to its annual yield of grapes in tons per acre over the period 1987-2016. Annual records of the following variables were available:

YIELD: Yield of grapes in the vineyards, in tons per acre.

RAIN: Centimetres of total rain fall during the rainy season.

DDAYS: Cumulative number of degree-days for the year. A degree-day is the number of degrees Fahrenheit above 50 degrees. Thus a day with 74 degree mean temperature would be a 24 degree-day. DDAYS is cumulated over the whole growing season.

TIME: Index of time, with TIME = 1 in 1987, 2 in 1988, 3 in 1989, etc.

FREEZE: Dummy variable coded 1 in those years in which there was a late killing frost.

NEW: Dummy variable that is coded 1 in those years in which a new management team was in place (i.e. 2007 to 2016).

Figure 1 (attached) present's descriptive statistics for the data and Figure 2 shows the correlation matrix of the variables. Figures 3 to 5 give various regression analysis output produced using Excel. Address the following questions, making sure to describe the modeling approach used:

(i) Explain in simple terms the meaning of the correlation coefficient between Rain and Yield in Figure 2.

(ii) Which variable(s) are significant at the 5% level in the regression model of Figure 3?

(iii) With reference to the model of Figure 3, what information is provided by the adjusted R² and the standard error of estimate?

(iv) Does the residual plot in Figure 4 highlight any problems with the model in Figure 3? If yes, how could they be resolved?

(v) What does the model in Figure 5 suggest for the effectiveness of the new management?

(vi) One vineyard manager claims that the effect of a late killing frost on the yield of grapes in the vineyard is a reduction of 0.7 of a ton per acre. Using the output for the model in Figure 5, perform a test to examine whether there is evidence to reject this suggestion (using a 5% significance level).

(vii) What are the strengths and weaknesses of the model in Figure 5? Suggest ways in which it could be improved.

Attachment:- Assignment File.rar

Reference no: EM131466329

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