ADMIN 2606 - Business Statistics: CRP Effectiveness Evaluation Report

This report evaluates the effectiveness of the Canada Rural Partnership (CRP), an initiative aimed at boosting the economy within rural areas of Ontario. The analysis focuses on data from 150 clients served by two consultancies, Innovation Pursuit (IP) and North Consultancy (NC). The report utilizes statistical methods, including paired and independent samples t-tests, to assess the impact of various services such as one-to-one consultations, workshops, and funding on client satisfaction. The findings indicate a positive correlation between funding and satisfaction, and a significant difference between one-to-one consultation and funding, suggesting that funding is more useful. The report also compares the performance of IP and NC, concluding that IP did not perform better than NC. The analysis provides valuable insights into the CRP's impact and offers recommendations for future improvements. The report also provides references to the sources used to write the assignment.

EFFECTIVENESS OF CANADA RURAL PARTNERSHIP (CRP)

Introduction
Rural economy is a prime indicator of a countries or region’s economic development (Benjamin,
2017). The aim of this report is to evaluate CRP’s effectiveness based on Innovation Pursuit (IP)
and North Consultancy (NC) clients. The general aim of the project is to boost the economy
within the rural areas of Ontario.
To achieve the aim of this aim, several analyses are carried out as follows;
Data analysis
Paired samples t test was used to compare population means for two samples drawn from the
same population (Van Hout, 2017). The independent samples t test was used for comparing
sample means from different populations (Pandis, 2015).
Descriptive analysis
The mean effectiveness value of one to one consultation is 3.1684, implying that on average, one
to one consultation is effective. The standard deviation is about 0.9188 implying that the data are
closely concentrated around the mean.
The mean effectiveness value of workshops is 3.2753, implying that on average, workshops are
effective. The standard deviation is about 0.9287 implying that the data are closely concentrated
around the mean.

Correlation
0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5
0.00
1.00
2.00
3.00
4.00
5.00
Association between satisfaction
and funding
Y
Predicted Y
Funding
Satsisfaction
The correlation coefficient between funding and satisfaction after consultation is 0.99502
implying that funding positively affects satisfaction. An increase in funding by 1 unit results to a
corresponding increase in satisfaction by about 0.99502 units.
Mean difference: Satisfaction prior to receiving consultation and after receiving
consultation
We test the hypothesis;

H0: mean<=3
H1: mean>3
On carrying out the paired samples t test, we get a p-value of 0.000852 for a one-tailed
distribution. Since p-value is less than the critical value, 0.05, we reject the null hypotheses that
the sample mean after consultation equals or less than 3. We would therefore expect the
participants level of satisfaction to rise after consultation.
Mean difference: Funding and one to one consultation
We test the hypothesis;
H0: There is no mean difference
H1: There is mean difference
On performing independent samples t test to test for difference in means between funding and
one to one consultation, we obtain a p-value that is less than 0.05 implying that we reject the null
hypothesis on no difference in means. We therefore conclude that there is significant mean
difference between one to one consultation and funding. Since the mean of funding is greater
than that of one to one consultation, it is reasonable enough to infer that funding is more useful
than one to one consultation.
We test the following hypothesis;
H0: IP did not perform better than NC
H1: IP performed better than NC

Mean difference: IP and NC
On performing independent samples t test to test for difference between the means of IP and NC,
we obtain a p-value of 0.4264 which is greater than the critical value 0.05. This implies that we
do not reject the null hypothesis that IP did not perform better than NC. We therefore reject the
claim that IP performed better than NC.

References
Benjamin, O. P. (2017). The economic contribution of a recreational fishery in a remote rural economy.
10.
Pandis, N. (2015). Book cover Comparison of 2 means (independent z test or independent t test). 2.
Van Hout, R. (2017). The paired t test and beyond. 6.