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- Statistics for the Business Sciences
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Browse the full collection of course materials, past exams, study guides and class notes for STAT 1430 - Statistics for the Business Sciences at Ohio State University verified by …
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Judit Bach
spring
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Class Notes
Taken by our most diligent verified note takers in class covering the entire semester.
STAT 1430 Lecture Notes - Lecture 3: Bar Chart
Bar graph showing percentage in each category (relative frequency bar graph: frequencies: number in each category. Bar graph showing number in each cat
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STAT 1430 Lecture Notes - Lecture 4: Frequency Distribution, Histogram, Standard Deviation
Xi n: xi is the collected data, n=sample size, e. g. n=1 x1=1, x2=2, x3=3, x =! =6/3=2: golf example: the sample mean for this data set is 93. 9 stroke
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STAT 1430 Lecture Notes - Lecture 5: Scatter Plot, Simple Linear Regression, Pearson Product-Moment Correlation Coefficient
Step 1: what can help predict coffee sales: availability/easy to get, game attendance, sales of other products, temperature . Collect data: make a scat
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STAT 1430 Lecture Notes - Lecture 6: Pearson Product-Moment Correlation Coefficient, Descriptive Statistics, Regression Analysis
Criteria for best line: smallest sse: sse= sum of the square for error, find the line with the smallest sse, from all the potential lines that go throu
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STAT 1430 Lecture Notes - Lecture 7: Scatter Plot, Aspirin
Residual plot l a u d i s e. Problems found: patterns and fans: fan pattern, curve pattern. Wal-mart: within the data set y away from the other points.
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STAT 1430 Lecture 9: Two-way Table February 05, 2019
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STAT 1430 Lecture 10: Probabilities Rules February 07, 2019
Stat1430 lecture 10 probability rules february 7th, 2019: agenda, basic terms and concepts, how to calculate probabilities. Complement rule: special ca
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STAT 1430 Lecture Notes - Lecture 11: Venn Diagram, Telemarketing, Brutus Buckeye
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STAT 1430 Lecture Notes - Lecture 12: Conditional Probability, Bayes Estimator
Next level: same probabilities, tougher conditions, work with what you"re given, get more data if you need it, tools to set up the problem: trees and t
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STAT 1430 Lecture 13: Random Variables February 19,2019
Setting things up: tree: in the tree, we can multiply branches to get and probabilities (which show on the leaves, p(a)*p(b|a)=p(a&b, p(a)*p(notb|a
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STAT 1430 Lecture Notes - Lecture 14: Random Variable, Gm Family 0 Engine, Weighted Arithmetic Mean
The mean of a discrete random variable: a weighted average of the possible outcomes; weights are the probabilities. Possible outcomes: x1, x2, xk call
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STAT 1430 Lecture 15: Random Variables February 26, 2019
Rule #3 for variances sums **(x an y are not independent)*: if x and y are not independent, they have a correlation, ! ( rho , then variance of x+y is:
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STAT 1430 Lecture Notes - Lecture 16: Probability Distribution, Antiderivative, Probability Density Function
Finding probabilities for continuous random variables: the probability for x within a certain interval is the integral of f(x) over that interval, for
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STAT 1430 Lecture Notes - Lecture 17: Probability Distribution, Random Variable, Standard Deviation
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STAT 1430 Lecture Notes - Lecture 18: Standard Deviation, Random Variable, Percentile
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STAT 1430 Lecture 21: Binomial Distribution March 19, 2019
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STAT 1430 Lecture Notes - Lecture 22: Binomial Distribution, Random Variable, Statistic
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STAT 1430 Lecture Notes - Lecture 23: Statistic, Sampling Distribution, Statistical Parameter
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STAT 1430 Lecture Notes - Lecture 24: Central Limit Theorem, Confidence Interval, Standard Deviation
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STAT 1430 Lecture Notes - Lecture 25: Confidence Interval, Sample Size Determination, Statistical Parameter
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STAT 1430 Lecture Notes - Lecture 26: Null Hypothesis, Alternative Hypothesis, Confidence Interval
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STAT 1430 Lecture Notes - Lecture 27: Yogurt, Test Statistic, Central Limit Theorem
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STAT 1430 Lecture Notes - Lecture 28: Jury Trial, Statistical Hypothesis Testing, Standard Error
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STAT 1430 Lecture Notes - Lecture 29: Outlet Store, Consumer Reports, Standard Score
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STAT 1430 Lecture Notes - Lecture 30: Statistical Hypothesis Testing, Response Bias, Test Statistic
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