# Statistical Hypothesis Testing and Key Performance Indicators

Pages: 5 (1256 words) Published: March 22, 2014
Executive summary
The following report aims to analyse and interpret the data set of 200 records regarding the CCResort. The given information includes booking identification number, income, number of people per booking, length of stay, age and overall expenditure. From the booking ID it can be assumed that the selection of data is random, however as it is only partial information and not the population, the period of time in which the data is selected from would affect the end results of analysis. The report is divided into two sections outlining the statistical analysis of data and hypothesis testing to observe if CCResort have met their 2 major key performance indicators (KPIs) 1More than 40% of their customers stay for a full week (i.e. seven nights); 2The average customer spends more than \$255 per day in excess of accommodation costs.

Figures at a glance
This section of the report aims to give users a better understanding of the data through statistical data analysis of investigation categories including family income, expenditure habits, age distribution, the number of people per booking and their length of stay. These analysis are meaningful in giving users a better understanding of the customer base in relation to the key performance indicators. 1. Family income distribution

From the data collected, 62 families (31% of the sample) earn an income of more than \$100,000 while 69% of the sample (138 bookings) had income less than \$100,000. From the group that had an income of less than \$100,000, the average number of people per booking is 3.2581, the average age of the group is 40.9032, the average length of stay is 2.5806 and the average expenditure per day is calculated to be \$208.9798. From the group with income more than \$100,000, the average number of people per booking is 3.6232, the length of stay is 5.7754, the average age is 48.0217 and the average expenditure is \$248.3643 per day.

2. Relationship between family income and expenditure
The analysis of expenditure is divided into three sections: total expenditure per day, expenditure for bookings with income less than \$100,000 and for bookings with income more than \$100,000. The total minimum expenditure per day \$147 dollars and a maximum of \$477.85. The average expenditure of the total expenditure per day is \$236.15.

For the group with income less than \$100,000(62 bookings), the minimum expenditure per day is \$147 and the maximum expenditure is \$298.50. The average expenditure for the group is \$208.9798 (approximately \$209). The histogram is positively skewed suggesting that there is a negative relationship between expenditure and income.

For the group which declared that they have more than \$100,000 income (138 bookings), the minimum expenditure per day is \$173.8571 while the maximum expenditure is \$477.8571. The average expenditure for this group is \$248.3643. As seen from the histogram below the data is positively skewed with an outlier, this can be identified as irregular but is not necessarily an error.

It can be concluded after analysing the different data that the expenditure is dependent with the income earned where the average daily expenditure for the group with income greater than \$100,000 has a higher average expenditure (\$248.3643) in comparison to the group with lower income (\$208.9798). The outlier of \$477.8571 per day should be accounted as it skews the data which may lead to the misrepresentation of the data.

3. Age distribution
The age distribution of the sample data is analysed with the following statistical indicators: Mean45.815
Median45
Mode43
Range36
Standard Deviation9.76512
Variance95.35756
Skewness0.083379

The customers’ age distribution gives CCResort a better understanding of the type of customers has been attracted to the resort by interpretations of the age demographic through the data. The above table shows that...