Introduction
Data analysis is critical to enhancing effective decision-making in sports. As a data analyst in a basketball team, the major problem to be solved is to conduct a comprehensive analysis of historical data, find patterns in team behavior, and use it to improve performance. The data set will analyze the NBA for the Philadelphia 76ers between 1997 and 1999 and the Los Angeles Lakers from 2017 to 2019. The statistical method used is linear regression embedded in Python to determine the relationship between independent and dependent variables.
Your Team and the Assigned Team
The team picked for the analysis was the Los Angeles Lakers (LAL) between 2017 and 2019. The team assigned was the Philadelphia 76ers from 1997 to 1999. The team information is summarized in Table 1 below;
Table 1 – Information on the Teams.
Hypothesis Test for the Population Mean (I)
Hypothesis testing is generally used to evaluate all kinds of claims about a specified parameter in the team. A hypothesis test is completed in six, starting with formulating the null and alternative hypotheses. A null hypothesis is a claim considered authentic until there is enough evidence to disapprove it. An alternative hypothesis (controverts the assumed null hypothesis (Shestakov, 2021).
The second step is determining the significance level (α), which will be used to accept or reject the claim. Thirdly, the test statistic, such as the t-test, is chosen in Python. The fourth step is the determination of the rejection region. The fifth step is calculating the test statistic and the p-value for comparison.
Finally, the significance level and p-value are compared to make inferences. When p ≤ α, the null hypothesis is rejected and accepted when p > α. For the given scenario at a significance level of 0.05, Python calculates the p-statistic and p-value as shown in Table 2 below:
Table 2 – Hypothesis Test for the Population Mean (I).
In the given hypothesis test, the first statement to support the claim is that the relevant skill level for the Los Angeles Lakers is assumed to be 1340. However, the management believes that the team can do better than that. The team thinks the skill level is above 1340 (: µ > 1340. It is important to note that the null hypothesis is rejected since the p-value is 0.0452 and is smaller than 0.05 or the significance level (Shestakov, 2021). The implication is that the team managers are correct that the team has a greater capacity to perform better.
Hypothesis Test for the Population Mean (II)
The null hypothesis for the test will be given below. The alternative can be given as more than that, stating that the team had scored points greater than 106, as provided by. A 1% level of significance was used, meaning that α=0.01. Table 3 shows the test statistic and the p-value.
Table 3 – Hypothesis Test for the Population Mean (II).
Similarly, since the given p-value of 0.0097 is smaller than t0.01, the LAL scored fewer points than projected, and the management must, therefore, work harder to obtain better results for the team.
Hypothesis Test for the Population Proportion
The population portion is a unique method used to determine whether the population under study is similar to the hypothesized. In the case given, H0: P = P0 is the null hypothesis and states that the Los Angeles Lakers can win 0.90, equivalent to 90% (H0: P = 0.9) when they score more than 102 points. On the other hand, the alternative hypothesis states that the Los Angeles Lakers cannot win even if they score more than 102 points. When the stated level of significance is 5%, the value of statistical values is as the ones given in Table 4 below:
Table 4 – Hypothesis Test for the Population Proportion.
When the p-value of 0.0321 is compared to the significance level at 0.05, it reiterates that there is enough evidence to prove that there is a 90% chance that LAL can only win if it scores more than 102 points.
Hypothesis Test for the Difference Between Two Population Means
Hypothesis testing can also test whether two given data sets are equal. According to the information, the LAL Lakers had an equal skill level as the Philadelphia 76ers. The hypothesis is, therefore, given as H0: µ1 = µ2. The alternative hypothesis means that the two are not equal, as given by Ha: µ1 ≠ µ2. When the significance level is 0.01, Table 5 shows the statistical and p-values.
Table 5 – Hypothesis Test for the Difference Between Two Population Means
Since the p-value is 0.0356, there is enough evidence to prove that LAL has fewer skills than the Philadelphia 76ers. The coach and other team managers must work harder to improve the team’s skills.
Conclusion
The four hypothesis tests using the given tool had p-values of 0.0452, 0.0097, 0.0321, and 0.0356, respectively. The obtained values were significantly impacted as they were always less than the significance levels. Consequently, it could easily be determined whether a hypothesis was proven. According to the outcomes, the Los Angeles Lakers, between 2017 and 2019, had better chances of winning, but more effort was needed, especially when scoring the required points. The practical importance of the performed analyses was to inform the team where more effort was required.
Citation
Shestakov, D. (2021). The Hypotheses Testing Method for Evaluation of Startup Projects. Journal of Economics and Management Sciences, 4(4), 47-47. Web.
Loading...
