One Sample Hypothesis Testing Paper

From the above output, we can see that the p-value is 0.000186, which is smaller than 0.05 (if we select a 0.05 significance level).

15 In testing the hypotheses: H0 β1 ’ 0: vs. H1: β 1 ≠ 0 , the following statistics are available: n = 10, b0 = 1.8, b1 = 2.45, and Sb1= 1.20. The value of the test statistic is:

The null hypothesis was that the female and male shoe sizes have an equal mean while the alternative hypothesis was that female and male shoe sizes do not have an equal mean. With the degrees of freedom being 33, the t-statistic is -8.27. The probability that -8.27 is ≤-1.69 is 7.5×10-10 for the one-tailed test. Also, the probability that -8.27 is ≤ ±2.03. is 1.5×10-9 for the two-tailed test. Due to both probabilities being under the alpha value of 0.05, the null hypothesis is rejected, and the alternative hypothesis is accepted at the 95% confidence level.

13. For the scores (3.0, 3.4, 2.6, 3.3, 3.5, 3.2): N = 6; Sum = 19; Mean = Sum/N = 19/6 = 3.17; Median = (2.6+3.3)/2 = 2.95; Sum of Squared Deviations = (3-3.17)2+(3.4-3.17)2+(2.6-3.17)2+(3.3-3.17)2+(3.5-3.17)2+(3.2-3.17)2 = 0.533; SD2 = SS/N = 0.533/6 = 0.089; SD = 0.089 = 0.298.

I will be examining Forbes’s list of most expensive NHL franchises based on data collected up until the 2013-2014 season. This particular form of popular culture is the National Hockey League or the professional ice hockey sport. This is an important topic to examine, because these are multi-million dollar (in some cases billion dollar) organizations/franchises where lots of revenue is made daily, affecting the North American economy greatly. Although some teams are worth much more then others, how well the team performs doesn’t directly affect the teams overall worth, rather time spent in the league, and location of each given team act as the biggest factors.

A two-tailed t-test yields a t-value of 0.99. For 13 degrees of freedom, t-critical is 2.16 for a 95% confidence level and 1.35 for an 80% confidence level. This is reflected in Table 1 below.

There was no definitive conclusion that there is a difference in speed that would correlate to the size of the isopod. For the next portion of the experiment a T-test for the speed vs size was conducted. The important numbers and variables are as follows: The degrees of freedom was 28. The T-stat value is 0.9944. The T-critical two tail number was 2.048. The P-value two tails came out to 0.3285. The P-value is less than 0.05 so it is not significant. This is due to random chance. The T-stat value is smaller than the t-critical for the two tailed test so in this situation we would accept the null

The degrees of freedom of the chi-square is (# of rows-1) * (# of columns-1) or (2-1) * (3-1) = 2

In investigating the impact of the regime changes on league competitiveness, the overall change in league equilibrium is charted based on the standard deviation of the team winning percentages for a particular season. One can compare the winning percentages’ actual standard deviation (ASD) with the ideal standard deviation (ISD) in a given year, assuming a perfectly competitive league. This approach was first used by Roger Noll and Scully in 1988 in their studies where they argued that the dispersion of wins could be measured by comparing the actual performance of a league to the performance that would have occurred if the league had the maximum degree of competitive balance in the sense that all teams were equal in playing strengths. The

The topics they would cover are; the number of lottery picks that team has had in the draft over the years, the number of all-star players a teams has, or how salary caps effect professional sport team’s performance. I have also seen studies that have taken all stats from players to see what makes each team successful when it comes to winning championships (cite these). In contrast, this paper examines success at the individual or player level. By assessing which statistics have the highest rate of return, this paper will help current and future NBA players better understand the most valuable contributors to

The t-test result for the total risk score is the largest one (4.14) and therefore represents the largest relative difference between the pretest and 3-month tests. For a sample size of 21 and a significance level of 0.05, a t-test result greater than 2.09 (or less than -2.09) is statistically significant. I do not know why table 2 doesn't have an asterix on the t-test result of 4.14 to indicate that it is significant.

The Two One Side Tests is the process used to validate the Physics teachers claim. Once running two t-Test with an equal variance value of 0.05 representing the alpha or significance level. For the TOST test the hypothesized mean value equal to 0.0538. (StatTrek, 2017). For this exercise, the hypothesized mean with a value of 420 with the alpha equal to 0.005 (Figure 18).

How confident are you of your estimate? Explain. Not Very. The F-statistic is far below the

In the beginning of the movie, the basic meaning of economics is explained. We learn that economics examines how people use scarce resources to fulfill their boundless wants and desires and how decisions are made when dealing with such scarcity (Krugman, Wells, 2016). To win the championships, it is known that you need to build a team of the best players. Unfortunately for the Oakland A’s they face a financial restriction and cannot afford the players they want. The team is known to be one of the poorest teams in baseball during the years illustrated. Losing two of the team’s best players, the Oakland A’s need to find alternative players without spending money they do not have. The team tries a new approach to help increase productivity by using statistical analysis instead of a visual approach and other ways the professional athletic committees use to draft players (Moneyball, 2011).

The significance of earnings is a growing façade in today’s economy. Daily operation, individuals, and families alike rely heavily on each sale or paycheck to provide financial stability throughout. Depending on the nature of labor, wages are typically compensated in accords to one’s experience and education or specialization. Moreover, calculating the specified industry, occupation title, education, experience on-the-job, gender, race, age, and membership to a union will additionally influence wages. To help analyze operation pay scales and remain within budget a business should obtain data pertaining to current variations in wage. Today statistics allow a business or businesses to do so in a timely and