HOSPITALITY INDUSTRY

The Hospitality Industry

The Hospitality Industry

Part 1

Derive a table of correlations to show the values of the correlation coefficients between “total employment costs” and each of the other variables.

Correlations

Total employment costs (£million)

Number of enterprises (number)

Total turnover (£ million)

Total purchases of goods, materials and services (£ million)

Total employment costs (£million)

Pearson Correlation

1

.964**

.998**

.993**

Sig. (2-tailed)

.000

.000

.000

N

12

12

12

12

Number of enterprises (number)

Pearson Correlation

.964**

1

.971**

.984**

Sig. (2-tailed)

.000

.000

.000

N

12

12

12

12

Total turnover (£ million)

Pearson Correlation

.998**

.971**

1

.998**

Sig. (2-tailed)

.000

.000

.000

N

12

12

12

12

Total purchases of goods, materials and services (£ million)

Pearson Correlation

.993**

.984**

.998**

1

Sig. (2-tailed)

.000

.000

.000

N

12

12

12

12

**. Correlation is significant at the 0.01 level (2-tailed).

From the above correlation table it can be observed that the correlation between Total employment costs (£million) and Number of enterprises (r = 0.964, P = .000), Total employment costs (£million) and Total turnover (£ million) (r = 0.998, P = 0.000) and Total employment costs (£million) and Total purchases of goods, materials and services (£ million) (r = 0.993, P = 0.000) reported in the table is positive and significantly different from 0 because the p-value of 0.000 is lower than 0.05.

2) Determine which single variable would be the best predictor for “total employment costs”.

From above correlation matrix it can be observed that the correlation value among the Total employment costs (£million) and Total turnover (£ million) is high (i.e. r = 0.998). So it can be said that these two variables are strongly correlated and the change in one variable may directly affect the presence of other variable. It is conclude that it is a best predictor for “total employment costs”.

Derive a scatter graph to show the relationship between the chosen predictor and “total employment costs”.

From the above scatter graph it can be observed that the data points of best predictor variable total turnover and total employment costs is strongly correlated as the data points are very close to each other.

Determine the coefficient of determination and the regression equation linking “total employment costs” and your chosen predictor.

The coefficient of determination (R ²) is an indicator for judging the quality of a linear regression, single or multiple. On a value between 0 and 1, it measures the fit between the model and observed data. While the R² has its flaws but its usefulness is matched only by its simplicity.

Model Summaryb

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.998a

.995

.995

441.249

a. Predictors: (Constant), Total turnover (£ million)

b. Dependent Variable: Total employment costs (£million)

From above table, it is observed that the value of coefficient of determination is 0.995, so it can ...