Generally speaking dispersion is the difference between the actual value and

# Generally speaking dispersion is the difference

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Generally speaking, dispersion is the difference between the actual value and the average value. The larger this dispersion or variability is, the higher the standard deviation. The smaller this dispersion or variability is, the lower the standard deviation. Chartists can use the standard deviation to measure expected risk and determine the significance of certain price movements.STDEV.S is used when the group of numbers being evaluated are only a partial sampling of the whole population. The denominator for dividing the sum of squared deviations is N-1, where N is the number of observations ( a count of items in the data set ). Technically, subtracting the 1 is referred to as "non-biased." STDEV.P is used when the group of numbers being evaluated is complete - it's the entire population of values. In this case, the 1 is NOT subtracted and the denominator for dividing the sum of squared deviations is simply N itself, the number of observations ( a count of items in the data set ). Technically, this is referred to as "biased." Remembering that the P in STDEV.P stands for "population" may be helpful. Since the data set is not a mere sample, but constituted of ALL the actual values, this standard deviation function can return a more precise result.Confidence Intyerval: A term used in inferential statistics that measures the probability that a population parameter will fall between two set values. The confidence interval can take any number of probabilities, with the most common being 95% or 99%. In other words, a confidence interval is the probability that a value will fall between an upper and lower bound of a probability distribution. For example, given a 99% confidence interval, stock XYZ's return will fall between -6.7% and +8.3% over the next year. In layman's terms, we are 99% confident that the return's of holding XYZ stock over the next year will fall between -6.7% and +8.3%.Create an X-Y Scatter Diagram (Scatterplot) for the data. Insert a trend line. Also include the regression equation and the R2 on the chart.Advertising Budget\$0 \$20,000 \$40,000 \$60,000 \$80,000 \$100,000 \$120,000 \$140,000 \$160,000 \$180,000 0,00020,00040,00060,00080,000100,000120,000f(x) = 0.5429839096x + 5221.0977409461R² = 0.7081366662Relationship of Budget Dollars to Product SalesUnit Sales \$136,297 98,715\$139,114 75,886\$165,575 83,3606Given the strong linear relationship between the advertising budget and uni sales in the previous example, can it be said that the budgeted dollars caused the sales?BA for Yes; B for No.7Use Excel's regression tool to perfrom a regression analysis for the budged and sales data in the above dataset. Treat the advertising budget as the independent variable.  #### You've reached the end of your free preview.

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