If the cholesterol level of healthy men is normally distributed with a mean of 180 and a standard deviation of 20, and men with cholesterol levels over 225 are diagnosed as not healthy, what is the probability of a type one error? z=(225-180)/20=2.25; the corresponding tail area is .0122, which is the probability of a type I error. If the cholesterol level of healthy men is normally distributed with a mean of 180 and a standard deviation of 20, at what level (in excess of 180) should men be diagnosed
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Final Project You are required to complete a final project. Please consult the Course Calendar for the due date. Project Description Statistics is about more than calculations. It is about turning data into information and using this information to understand the population. A statistician will be asked to help solve real world problems by designing a study, collecting data, analyzing the data, and writing up the results. As a final project, you will be asked to do something similar. Though the
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Assignment 1: Making Decisions Based on Demand and Forecasting Regression analysis is the description about the relationship between two variables where one is dependent and the other is independent. Regression analysis (in statistics), generally, is about any techniques that facilitate modeling and analysis of several variables. It focuses on the relationship between a dependent variable and one or more independent variables (Sykes, 2000). To be specific, regression analysis
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ForecastX Software (There will be additional ForecastX notes for subsequent chapters) 1. Insert disk and install a. Note: you will have to do this every time you use a campus computer, but the process should only take a minute or two. b. Insert disk. If a McGraw Hill screen pops up, just agree to what it is asking you, and then reduce the window. c. Double click on “My computer” d. Select (click once on) cdrom drive (or whatever the listing is for the drive
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Write a brief paper on Statistics and Operations Research in the context of Analytics. Analytics is defined as the scientific process of transforming data into insight for making better decisions. It is the combination of skills, technologies, applications and processes used by data scientist to gain insight in to their business based on data and statistics to drive business planning. It typically use’s data, statistical and quantitative analysis to measure the performance of the subject (Organization/
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P(t25 Critical value: Statistic= t=(26.067-25)/(1.534/√15)=2.694 Critical value= inv-t(14,0.01) =2.624 Test value: Reject region: {t/t>2.624} Decision: statistic=2.694>2.624=critical value We reject H0, At the .01 significance level we conclude that the mean number of patients per day is more than 25. Decision: P-value= t(2.694,14)=0.0087 So p-value < 0.01 (we reject H0) Summary: We can interpret the p-value as: p-value= probability of obtaining a statistic value greater than 2.694
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Statistics Statistics is used more often than people realize. They are used for many reasons such as to help one make a difficult decision in their personal or professional life. Statistics is also used to help companies promote their merchandise. Have you ever seen a commercial that used numerical information to show viewers that their product is preferred over their competitors’ product? That is just one of the many times one has probably seen statistics used without even realizing it. Statistics
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Descriptive Statistics Once the Data collection process has been completed the researcher must then try to make sense of the information collected. The data at this stage will be in its raw form and will generally not be suitable for presentation or interpretation The purpose of descriptive statistics, as the name suggests, is to describe a set of data. They are used to provide manageable summaries of data sets. They are the simplest and most widely used set of statistics and in many data
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UNIT #5 DISCUSSION DQ #1: Data collection is one of the most challenging steps that prevent learners from completing their dissertation. What challenges do you foresee in collecting your data? What are some strategies you’ll deploy to overcome these challenges? a) Challenges in collecting data Reliability and validity In data collection, reliability and validity of the data are important considerations, which a researcher should pay attention to. Reliability, in this case, can be described
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Statistics: Q # 4 I used Wages data set. Hypothesis Test: Independent Groups (t-test, pooled variance) | | | | | Married Age | No Married Age | | | 42.31 | 32.61 | mean | | 11.84 | 11.61 | std. dev. | | 67 | 33 | n | | | | | | 98 | df | | | 9.707 | difference (Married Age - No Married Age) | 138.411 | pooled variance | | 11.765 | pooled std. dev. | | 2.502 | standard error of difference | | 0 | hypothesized difference | | | | | | 3.880
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