Grader Instructionsexcel 2019 Projectexp19 Excel Ch08 Cap Golden St ✓ Solved

Grader - Instructions Excel 2019 Project Exp19_Excel_Ch08_Cap_Golden_State_5K Project Description: You are a volunteer for the Golden State 5k, an annual 5k held across several cities in California to raise money for at risk youth. As part of your duties, you track donations, volunteer information, and race results. This year you have decided to use Excel to calculate frequency distribution by age and time, calculate various descriptive statistics, and forecast participation rate as well as donation rate for 2025. Steps to Perform: Step Instructions Points Possible 1 Start Excel. Download and open the file named Exp19_Excel_Ch08_Cap_GoldenState5k.xlsx .

Grader has automatically added your last name to the beginning of the filename. Ensure the RaceResults worksheet is active, then use the FREQUENCY function to calculate the frequency distribution of the race results in column D. Place your results in the range G4:G9. Enter a function in cell F22 to calculate the correlation between age (Columns C) and race time (Column D). Enter a function in cell G22 to calculate the covariance between age and race time.

Enter a function in cell H22 to calculate the variance of the ages in the data set. Note this is a sample of data not a population. Enter a function in cell I22 to calculate the standard deviation of the ages in the data set. Ensure the Data Analysis ToolPak add-in is active. Use the Data Analysis ToolPak to create a histogram with chart output starting in cell H12 based on the ages of the runners surveyed.

Use the range F13:F18 as the Bin Range. Ensure that Chart output and Cumulative percentage is included in the results. Place the upper left hand corner of the chart in cell L13. Ensure the VolunteerInfo worksheet is active. Use the Data Analysis ToolPak to perform a single factor ANOVA on the range C5:E21 (Including column lables).

Place the results starting in cell G5. Create a Forecast Sheet that depicts year over year growth in participation for the city of Los Angeles. Set the Forecast end year as 2025 and place the results on a new worksheet named 2025Forecast . Ensure the Participants worksheet is active then create a scatter plot chart that places the Participant observations on the X axis and the Donation dollars on the Y axis (do not include column headings). Add the chart title Participant Forecast and a linear trendline to the chart that also shows the Equation and the R-square.

Enter a function in cell F6 to calculate the intercept of the linear trendline created in the prior step. Enter a function in cell G6 to calculate the Slope of the linear trendline. Enter a function in cell H6 to calculate the R-square of the linear trendline. Enter a function in cell I6 to calculate the Standard Error. Use the FORECAST.LINEAR function in cell F9 to forecast potential donations once the goal of 20,000 participants is reached.

Format the results as Currency. Complete your analysis by adding formulas in the range G9:H9 to calculate the high and low thresholds of the forecast. Save and close EXP19_Excel_Ch08_CAP_GoldenState5k . Exit Excel. Submit the file as directed.

0 Total Points 100 Created On: 11/20/2020 1 Exp19_Excel_Ch08_Cap - Golden State 5K 1.1 In a short paragraph of 3-7 sentences, briefly explain ONE fallacy you have noticed in the news or real life. Feel free to find an example in the news online if you do not know one offhand. Be sure to identify what type of fallacy it is. For example, an evangelical pastor, Minister Kevin Swanson, blamed hurricanes hitting Texas on Houston's mayor because she was pro-LGBTQ. Right-wing radio host Rush Limbaugh said that the hurricanes were caused by "liberal media hype to make people believe that climate change is real." Both are post-hoc fallacies, the fallacy of false-cause-and-effect.

RaceResults Golden State 5k Runner # Age Time Duration Frequency :18::14::16::28::28::43::14::57::57::12::27::26::22::28::17:16 Age :22::27::50::25::17::16::43:12 >:57::01:12 Correlation Covariance Variance Standard Deviation :19::01::04::35::03::19::04::04::33::04::37::04::04::48::04::37::26::35::48::26::43::04::37::04::14::25::17::37::33::33::04::18:43 VolunteerInfo Golden State 5k Location Combined Year Los Angeles San Diego San Jose ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,000 Participants Golden State 5k Location Los Angeles 20000 Year Participants Donations Intercept Slope RSQ Standard Error ,000. ,500. ,000.00 Forecast High Low ,000. ,500. ,000. ,000. ,100. ,000. ,250. ,125. ,500. ,000. ,000. ,000. ,500.00

Paper for above instructions

Excel Analysis for Golden State 5K

The Golden State 5K is an annual event aimed at raising funds for at-risk youth. As a volunteer, the objective of this assignment is to utilize Excel 2019 for tracking donations, race results, and volunteers. This report will detail the analysis using functions, statistical tools, and forecasting methods provided in Excel.

1. Frequency Distribution Calculation

To compute the frequency distribution of the race results, we use the FREQUENCY function in Excel. This function is particularly useful for determining how often certain ranges of values (bins) occur within a dataset. For the Golden State 5K results, the ages of the participants provided will be binned into categories placed from G4 to G9.
The formula we use in cell G4 is:
```excel
=FREQUENCY(D4:D100, F13:F18)
```
This formula counts the number of observations in `D4:D100` that fall within the ranges defined in `F13:F18` (i.e., the bin values). It provides insight into how age groups are represented in the race results, allowing the organization to tailor future events accordingly.

2. Statistical Function Applications

To analyze the correlation and covariance between age and race time, we input the following formulas:
- Correlation in cell F22:
```excel
=CORREL(C4:C100, D4:D100)
```
- Covariance in cell G22:
```excel
=COVARIANCE.S(C4:C100, D4:D100)
```
- Variance and Standard Deviation of ages in cells H22 and I22, respectively:
```excel
=VAR.S(C4:C100)
```
```excel
=STDEV.S(C4:C100)
```
These statistical calculations give insight into the relationship between the ages of participants and their race times, indicating how one might affect the other.

3. Histogram Creation

Using the Data Analysis ToolPak, we generate a histogram for visual representation of the age distribution. After selecting the Histogram option, input range (age data) and bin range (F13:F18) are required. Upon confirming the settings, the histogram will display the age distribution, aiding the organization in understanding demographic engagement.

4. ANOVA for Volunteer Information

Using the ANOVA function via the Data Analysis ToolPak, we assess whether there are significant differences in the average donations across different locations. The single factor ANOVA is performed on the range C5:E21, and the function is initiated, placing results starting from cell G5. This analysis can guide decision-making concerning fundraising efforts across cities.

5. Forecasting Participation

To forecast participation rates until 2025 for Los Angeles, we create a Forecast Sheet. By setting the end year to 2025, Excel will generate weekly predictions based on the previous years’ data. This forecast will be placed on a new sheet titled "2025Forecast" with participants projected for the coming years, enabling the organization to plan strategies accordingly.

6. Scatter Plot for Donations

In the "Participants" worksheet, a scatter plot is created displaying participant counts on the X-axis and donation dollars on the Y-axis:
1. Select the range excluding headers.
2. Insert a scatter chart.
3. Title the chart “Participant Forecast” and add a linear trendline with the equation displayed.
For regression metrics like intercept, slope, R-squared, and standard error, we utilize:
- Intercept in cell F6:
```excel
=INTERCEPT(D4:D100, C4:C100)
```
- Slope in cell G6:
```excel
=SLOPE(D4:D100, C4:C100)
```
- R-squared in cell H6:
```excel
=RSQ(D4:D100, C4:C100)
```
- Standard Error in cell I6:
```excel
=STEYX(D4:D100, C4:C100)
```
These functions yield a quantitative assessment of how well participant numbers predict donation amounts.

7. Future Donation Forecast

We then use the `FORECAST.LINEAR` function in cell F9 to predict future donations based on reaching a target of 20,000 participants:
```excel
=FORECAST.LINEAR(20000, D4:D100, C4:C100)
```
The results are formatted as currency for clear financial understanding. Additionally, we calculate high and low thresholds for the forecast in cells G9 and H9:
```excel
=F9 * 1.1 // high threshold
=F9 * 0.9 // low threshold
```

Conclusion

Using Excel not only streamlines the data analysis process but empowers the Golden State 5K event organizers to make informed decisions on fundraising and participant engagement based on empirical data. The thorough application of statistical tools, correlation analysis, and robust forecasting allows for strategic planning toward successful future events.

References

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