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Statistics for Business Analysts

The normal distribution is one of the most important continuous distributions in statistics. It closely fits many real-world phenomena and is useful for generalizing sample inferences to populations. The normal distribution is bell-shaped and symmetric, with the mean, median, and mode all in the center. ANOVA can test for significant differences among three or more sample means and is useful for comparing things like gasoline mileage or training methods. Central tendency measures the center of a data set and includes the mean, median, and mode. It should be easy to compute, understand, and not affected by outliers. Hypothesis testing involves formulating hypotheses, calculating standard errors, finding test statistics, and interpreting probabilities to make decisions about hypotheses.

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75 views2 pages

Statistics for Business Analysts

The normal distribution is one of the most important continuous distributions in statistics. It closely fits many real-world phenomena and is useful for generalizing sample inferences to populations. The normal distribution is bell-shaped and symmetric, with the mean, median, and mode all in the center. ANOVA can test for significant differences among three or more sample means and is useful for comparing things like gasoline mileage or training methods. Central tendency measures the center of a data set and includes the mean, median, and mode. It should be easy to compute, understand, and not affected by outliers. Hypothesis testing involves formulating hypotheses, calculating standard errors, finding test statistics, and interpreting probabilities to make decisions about hypotheses.

Uploaded by

Stany D'mello
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Download as DOCX, PDF, TXT or read online on Scribd
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Normal Distribution The most celebrated of the continuous distributions and plays an important role in statistical inference because

of primarily two reasons It has properties which help generalize inferences by taking samples to the entire population The Normal Distribution comes closest to fitting the actual observed frequency distributions of many phenomena including human characteristics (Height, Weight etc) and physical processes (Rainfall) and other measured of interest to Managers

Sampling methods Use of lottery Use of Random numbers Systematic sampling/ quasi random sampling Stratified Random sampling Disproportionate stratified sampling Cluster sampling Multistage sampling Area sampling ANOVA ANOVA will enable us to test for the significance of the differences among more than two sample means. Situations where it can be used : Analysis of variance is useful in such situations as comparing the mileage achieved by five different brands of gasoline , testing which of four different training methods produces the fastest learning record.

Characteristics The curve is smooth and is bell shaped It has a single peak and is unimodal The mean lies at the centre and the curve is symmetric about the mean Mean=Median=Mode and they all lie at the centre

Assumptions The samples are independently ( or randomly) drawn from the population All the population from which samples have been drawn are normally distributed The variances of all the population are equal.

The tails of the curve extend indefinitely in both directions from the centre and although they get closer and closer to the horizontal axis, they never quite touch it

Central Tendency Central Tendency may be defined as the parameter in a series of statistical observation, which reflects a central value of the same series. Major characteristics of an entire series of data reflected by a parameter called CENTRAL TENDENCY Properties of a Measure of Central Tendency It should be well defined. It should be easy to compute It should be easy to understand. It should be based on all observations. It should be capable of further mathematical treatment. It should not be affected by extreme observations. It should not be affected much by fluctuations of sampling. Basic steps of hypothesis testing Formulating the hypothesis Calculating the std error of the mean Find the z value or t value Interpreting the probability- associated with this difference The decision makers role in formulating hypothesis Risk of rejection

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