**Statistics Project_Data _Summary_Frequency_Histogram_Bar chart_Pie-Chart_Summary_Answer**

Statistics Project_Data _Summary_Frequency_Histogram_Bar chart_Pie-Chart_Summary_Answer

Statistics Project_Data _Summary_Frequency_Histogram_Bar chart_Pie-Chart_Summary_Answer

**Statistics Project_Data _Summary_Frequency_Histogram_Bar chart_Pie-Chart_Summary_Answer
**

1.) purpose of Statistics Project

2.) Data (provide the raw data used, quantitative AND qualitative (or categorical) variables, and cite the source) the sample size n must be at least 30, that is, greater than or equal to 30. Recall that in general a major objective prior to collecting, organizing, summarizing, and analyzing data is that we that we want to make inferences (or draw conclusions) about some target population based on our sample results! Random sampling of data from a population is a technique for increasing the likelihood (although not a guarantee!) that your data will be representative of the population. Simple random sampling.you can use technology (your calculator or a website) to select your SRS. If you have a massive amount of data, that is, counts in the hundreds,thousands, or more, it will be necessary to use technology to select your SRS of size 30 or more!Cite the source. Label data as appropriate.

3.) Summary of Topic, and Data The idea here is two-fold: (1) To share your interesting project idea, and (2) To give me a chance to give you a brief thumbs-up or thumbs-down before you finish the project. Sometimes people get off on the wrong foot or misunderstand the intent of the project, and your posting provides an opportunity for some feedback. Remark: Students may use similar topics, but must have different data sets. For example, several students may be interested in the same topic or aspect of a business and that is fine, but they must collect different data, perhaps from different years.

4.) Frequency and Relative Frequency Distribution Tables for a quantitative and a qualitative variable by hand, or by using statistical software such as Excel or some other software (much preferred in the business and government world!)

NOTE: Recall to properly TITLE your table, label columns, and include column totals for frequencies and relative frequencies.

5.) Histogram (for quantitative aka numerical or metric data) by hand, or you can earn up to 5 points extra credit (WOW!) if you construct a histogram (in color would be nice!) for grouped quantitative data or single-value quantitative data) using Excel or some other statistical software (much preferred in the business and government world!

NOTE: Recall to properly TITLE your histogram and to properly label the x- and y-axes of your histogram.

6.) Bar chart (aka bar graph) for qualitative (or categorical) variable by hand, or you can if you construct a bar chart (in color would be nice!) using Excel or some otherstatistical software(much preferred in the business and government world!).

NOTE: Recall to properly TITLE your bar chart (or bar graph), and to properly label the x- and y-axes of your bar chart (or bar graph).

7.) Pie-Chart (or Pie-Graph) for your qualitative data by hand, (WOW!) if you construct a Pie-chart (in color would be nice!) using Excel or some other statistical software(much preferred in the business and government world!)

NOTE: Recall to properly TITLE your Pie-chart (or Pie graph) and to properly label the sectors (categories) of your Pie-chart by using frequencies, relative frequencies, or percents.

8.) For your quantitative data, calculate median, five-number summary, sample mean, range, sample variance, and sample standard deviation (SHOW WORK/EXPLANATION!). PLEASE Results can be confirmed by using using statistical software, or STAT MODE on your scientific calculator!

construct a boxplot (s), using fences, if possible, using Excel or some other statistical software (much preferred in the business and government world!) to identify potential (suspect) or probable (highly suspect) outliers. Recall outliers are extremely small or extremely large data values which may be: actual bona fide observations; data values from a different population(s), measurement errors, or recording errors, for example, transposing digits, that is, recording a data value as an 81, instead of an 18. Recall that outliers sometimes require special attention as they can grossly affect the value of a sample mean or sample standard deviation which could possibly alter subsequent data analysis and conclusions. Outliers may be the most interesting and thought provoking of all your observations!!

9.) Distribution of data: Calculate the percentage of data within one standard deviation of the mean, percentage of data within two standard deviations of the mean, percentage of data within three standard deviations of the mean (include explanation and interpretation). Recall that according to the Empirical Rule, for a bell-shaped distribution, the respective percentages are approximately 68%, 95%, and 99%. Do your percentages imply that your sample data distribution is approximately bell-shaped (or normally distributed)? Note that the answer could be Yes or No, depending on your data. You can also look at the shape of your histogram (is it roughly bell-shaped or normally distributed?) as well as the percentages when making your judgment.

10.) Results and Conclusion (A short narrative summary) Interpret your results in a narrative summary consisting of several paragraphs. Be sure to describe features of the graphs and measurements that you find to be important or interesting.

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