One Sample Hypothesis - Tips
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The document outlines the general procedure for hypothesis testing, including defining the null and alternative hypotheses, calculating test statistics, and making decisions based on significance levels and critical values. It then describes a case study analyzing factors that may influence food servers' tips, such as day of the week, party size, gender, and time of shift. Two hypothesis tests are presented, finding that Saturdays and Sundays have the best tips, and that larger parties tip more on average than smaller parties. The recommendation is to schedule food servers on a rotating basis across days and shifts to avoid unfair treatment.
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One Sample Hypothesis Tips
The document outlines the general procedure for hypothesis testing, including defining the null and alternative hypotheses, calculating the test statistic, and making a decision based on significance levels and critical values. It then provides an example case study analyzing factors that may influence food servers' tips, such as day of week, party size, gender, and time of shift. Two hypothesis tests are conducted. The first finds Saturday and Sunday are the best days for tips. The second finds that larger parties tip better than smaller parties, on average. Based on the results, the recommendation is to schedule food servers on a rotating basis across days and shifts to ensure fair treatment.
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One Sample Hypothesis - Tips
The document outlines the general procedure for hypothesis testing, including defining the null and alternative hypotheses, calculating test statistics, and making decisions based on significance levels and critical values. It then describes a case study analyzing factors that may influence food servers' tips, such as day of week, party size, gender, and time of day. Two hypothesis tests are presented, finding that Saturdays and Sundays have the best tips, and larger parties tip more on average than smaller parties. The conclusion recommends scheduling servers on alternating weekdays and shifts to avoid unfair treatment.
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Minimum
Maximum
Mean
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4.5771
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State them in English
Then in math
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Statistical ProcessesCan descriptive statistical processes b.docx
Statistical Processes
Can descriptive statistical processes be used in determining relationships, differences, or effects in your research question and testable null hypothesis? Why or why not? Also, address the value of descriptive statistics for the forensic psychology research problem that you have identified for your course project. read an article for additional information on descriptive statistics and pictorial data presentations.
300 words APA rules for attributing sources.
Computing Descriptive Statistics
Computing Descriptive Statistics: “Ever Wonder What Secrets They Hold?” The Mean, Mode, Median, Variability, and Standard Deviation
Introduction
Before gaining an appreciation for the value of descriptive statistics in behavioral science environments, one must first become familiar with the type of measurement data these statistical processes use. Knowing the types of measurement data will aid the decision maker in making sure that the chosen statistical method will, indeed, produce the results needed and expected. Using the wrong type of measurement data with a selected statistic tool will result in erroneous results, errors, and ineffective decision making.
Measurement, or numerical, data is divided into four types: nominal, ordinal, interval, and ratio. The businessperson, because of administering questionnaires, taking polls, conducting surveys, administering tests, and counting events, products, and a host of other numerical data instrumentations, garners all the numerical values associated with these four types.
Nominal Data
Nominal data is the simplest of all four forms of numerical data. The mathematical values are assigned to that which is being assessed simply by arbitrarily assigning numerical values to a characteristic, event, occasion, or phenomenon. For example, a human resources (HR) manager wishes to determine the differences in leadership styles between managers who are at different geographical regions. To compute the differences, the HR manager might assign the following values: 1 = West, 2 = Midwest, 3 = North, and so on. The numerical values are not descriptive of anything other than the location and are not indicative of quantity.
Ordinal Data
In terms of ordinal data, the variables contained within the measurement instrument are ranked in order of importance. For example, a product-marketing specialist might be interested in how a consumer group would respond to a new product. To garner the information, the questionnaire administered to a group of consumers would include questions scaled as follows: 1 = Not Likely, 2 = Somewhat Likely, 3 = Likely, 4 = More Than Likely, and 5 = Most Likely. This creates a scale rank order from Not Likely to Most Likely with respect to acceptance of the new consumer product.
Interval Data
Oftentimes, in addition to being ordered, the differences (or intervals) between two adjacent measurement values on a measurement scale are identical. For example, the di ...
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