File Name: a first course in statistical methods by ott and longnecker 2004 .zip
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In statistics , the variance inflation factor VIF is the quotient of the variance in a model with multiple terms by the variance of a model with one term alone. It provides an index that measures how much the variance the square of the estimate's standard deviation of an estimated regression coefficient is increased because of collinearity. Cuthbert Daniel claims to have invented the concept behind the variance inflation factor, but did not come up with the name. Consider the following linear model with k independent variables:. This identity separates the influences of several distinct factors on the variance of the coefficient estimate:.
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Thank you for your assistance in helping to safeguard the integrity of the content contained in this Supplement. We trust you find the Supplement a useful teaching tool. The population of interest is the weight of the shrimp maintained on the specific diet for a period of 6 months. The sample is the shrimp selected from the pond and maintained on the specific diet for a period of 6 months.
The weight gain of the shrimp over 6 months. Since the sample is only a small proportion of the whole population, it is necessary to evaluate what the mean weight may be for any other randomly selected shrimps.
The amount of radioactivity at all points in the suspect area. The randomly selected points in the suspect area. The level of radioactivity in the suspect area. We want to relate the level of radioactivity of the points in the sample to the level in the whole suspect area.
Thus we need to know how accurate a portrayal of the population is provided by the points in the sample. All households in the city that receive welfare support. The households selected from the city welfare rolls. The number of children per household for those households in the city which receive welfare.
In order to evaluate how closely the sample of households matches the number of children in all households in the city receiving welfare. All football helmets produced by the five companies over a given period of time.
The helmets selected from the output of the five companies. The neck strength of players is extremely variable for high school players. Hence, the amount of damage to the neck varies considerably from player to player for exactly the same amount of shock transmitted by the helmet. The population of interest is the population of those who would vote in the senatorial campaign.
The population from which the sample was selected is registered voters in this state. The sample will adequately represent the population, unless there is a difference between registered voters in the state and those who would vote in the senatorial campaign. The results from a second random sample of 5, registered voters will not be exactly the same as the results from the initial sample.
Results vary from sample to sample. With either sample we hope that the results will be close to that of the views of the population of interest. The sampled population is all freshmen enrolled in HIST Yes, there is a major difference in the two populations. Those enrolled in HIST may not accurately reflect the population of all freshmen at his university.
For example, they might be more interested in history. Had the professor lectured on the American Revolution, those students in HIST would be more likely to know which country controlled the original 13 states prior to the American Revolution than other freshmen at the university.
The explanatory variable is level of alcohol drinking. One possible confounding variable is smoking. Perhaps those who drink more often also tend to smoke more, which would impact incidence of lung cancer. To eliminate the effect of smoking, we could block the experiment into groups e.
The explanatory variable is obesity. Two confounding variables are hypertension and diabetes. Both hypertension and diabetes contribute to coronary problems. To eliminate the effect of these two confounding variables, we could block the experiment into four groups e. The explanatory variable is the new blood clot medication. The confounding variable is the year in which patients were admitted to the hospital. Because those admitted to the hospital the previous year were not given the new blood clot medication, we cannot be sure that the medication is working or if something else is going on.
We can eliminate the effects of this confounding by randomly assigning stroke patients to the new blood clot medication or a placebo. The explanatory variable is the software program. Adding advanced mathematics courses to inner city schools will not solve the discrepancy between minority students and white students, since there are other factors at work.
A simple random sample could then be selected within each plant. This would provide information concerning the differences between the plants along with the individual opinions of the employees.
Both surveys may have survey nonresponse bias because an entire segment of the population those not at home cannot be contacted. Also, both surveys may have interviewer bias resulting from the way the question was posed e. Alumni men only? Alumni whose addresses were on file 25 years later would not necessarily be representative of their class. Alumni who responded to the mail survey would not necessarily be representative of those who were sent the questionnaires. The fact that higher income respondents would be more likely to respond bragging , and the fact that incomes are likely to be exaggerated, would tend to make the estimate too high.
Stratified sampling. Stratify by job category and then take a random sample within each job category. Different job categories will use software applications differently, so this sampling strategy will allow us to investigate that. Systematic random sampling. Sample every tenth patient starting from a randomly selected patient from the first ten patients. Provided that there is no relationship between the type of patient and the order that the patients come into the emergency room, this will give us a representative sample.
This method will allow us to examine the employment status for each degree type and compare among them. Simple random sampling. Once we find containers we will stop. Still it will be difficult to get a completely random sample. Factors: Location in orchard, Location on tree, Time of year Factor levels: Location in orchard — 8 sections; Location on tree — top, middle, bottom; Time of year — October, November, December, January, February, March, April, May Blocks: none Experimental units: Location on tree during one of the 8 months Measurement units: oranges Replications: For each section, time of year, and location on tree, there is one experimental unit, hence 1 replication.
Completely randomized design with 10 treatments software packages and 3 replications of each treatment. Latin square design with blocking variables position in kiln, day , each having 8 levels.
Design B. The experimental units are not homogeneous since one group of consumers gives uniformly low scores and another group gives uniformly high scores, no matter what recipe is used. Using design A, it is possible to have a group of consumers that gives mostly low scores randomly assigned to a particular recipe. This would bias this particular recipe.
Using design B, the experimental error would be reduced since each consumer would evaluate each recipe. That is, each consumer is a block and each of the treatments recipes is observed in each block.
This results in having each recipe subjected to consumers who give low scores and to consumers who give high scores. This would not be a problem for either design. In design A, each of the remaining 4 recipes would still be observed by 20 consumers.
In design B, each consumer would still evaluate each of the 4 remaining recipes. Use payroll records. Stratify by employee categories full-time, part-time, etc. Consider systematic selection within categories. Each state agency and some federal agencies have records of licensed physicians, professional corporations, facility licenses, etc. What staffing requirements are unfilled at this time or may become available when expansion occurs? Licensing boards may have this information.
Many professional organizations have special categories for members who are unemployed, retired, working in fields not directly related to nursing, students who are continuing their education, etc. Population growth estimates may be available from the Census Bureau, university economic growth research, bank research studies prevailing and anticipated load patterns , etc.
Health risk factors and location information would be available from state health departments, the EPA, epidemiological studies, etc. If nitrogen first: [N,P] [40,10], [40,20], [40,30], then [50,30], [60,30] [50,10], [50,20], [50,30], then [40,30], [60,30] [60,10], [60,20], [60,30], then [40,30], [50,30].
Generate a random permutation of the numbers 1 to 15 7 4 11 3 13 8 1 12 16 2 5 6 10 9 14 Go through the list and the first two numbers that appear in each of the four groups receive treatment L1 and the other two receive treatment L2. Bake one cake from each recipe in the oven at the same time. Repeat this procedure r times. The baking period is a block with the four treatments recipes appearing once in each block. The four recipes should be randomly assigned to the four positions, one cake per position.
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A FIRST COURSE IN STATISTICAL METHODS BOOK June 17th, - Ott Longnecker First Course Statistical Methods Solutions Pdf Free Download.
Download Business Research Methods. An unbiased sampling method is one that is not biased. Local Police Departments: Policies and Procedures, This report presents statistics on selected policies and procedures of local police departments, based on data from the Bureau of Justice Statistics' Law Enforcement Management and Administrative Statistics survey. In addition, a.
Fresh market tomatoes Solanum lycopersicum L. This water uptake can lead to internalization of various hazardous bacteria, including Erwinia carotovora Jones , the causal agent of bacterial soft rot. Fruit were held for 2, 8, 14, and 26 hours after harvest for the fall season and 2, 4, 6, 8, and 14 hours for the following spring season before water immersion. Mature green fruit were weighed, submerged in water for 2 min and then reweighed to determine water uptake.
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