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please help and be as detail as you can 1. Variation is a key statistic used in the most analytical studies. what are the implications of the high level of variations vs. low level of variation in a sample data 2. what are the implications Solution 1. In statistics, analysis of variance (ANOVA) is a collection of statistical models, and their associated procedures, in which the observed variance in a particular variable is partitioned into components attributable to different sources of variation. In its simplest form, ANOVA provides a statistical test of whether or not the means of several groups are all equal, and therefore generalizes t-test to more than two groups. Doing multiple two-sample t-tests would result in an increased chance of committing a type I error. For this reason, ANOVAs are useful in comparing two, three, or more means. 2. the act of implicating or the state of being implicated.
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Several terms are used in these analyses that you need to be familiar with to understand the analyses themselves and the results. Many will already be familiar to you.
Statistical significance: This indicates the probability that the differences found are a result of error, not the treatment. Stated in terms of the P value, the convention is to accept either a 1% (P ≤ 0.01), or 1 out of 100, or 5% (P ≤ 0.05), or 5 out of 100, possibility that any differences seen could have been due to error (Cortina & Dunlap, 2007).
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IMPORTANT TERMINOLOGY
Several terms are used in these analyses that you need to be familiar with to understand the analyses themselves and the results. Many will already be familiar to you.
Statistical significance: This indicates the probability that the differences found are a result of error, not the treatment. Stated in terms of the P value, the convention is to accept either a 1% (P ≤ 0.01), or 1 out of 100, or 5% (P ≤ 0.05), or 5 out of 100, possibility that any differences seen could have been due to error (Cortina & Dunlap, 2007).
Research hypothesis: A research hypothesis is a declarative statement of the expected relationship between the dependent and independent variable(s).
Null hypothesis: The null hypothesis, based on the research hypothesis, states that the predicted relationships will not be found or that those found could have occurred by chance, meaning the difference will not be statistically significant.
Effect size: This is defined by Cortina and Dunlap as “the amount of variance in one variable accounted for by another in the sample at hand” (2007, p. 231). Effect size estimates are helpful adjuncts to significance testing. An important limitation, however, is that they are heavily influenced by the type of treatment or manipulation that occurred and the measures that are used.
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The answer can be found in our basic probability review. We are concerned with the probability of a TYPE I error (rejecting a true null hypothesis). We generally set an alpha level of .05, which is the probability of making a TYPE I error. Now consider what happens when we do three t tests. There is .05 probability of making a TYPE I error on the first test, .05 probability of the same error on the second test, and .05 probability on the third test. What happens is that these errors are essentially additive, in that the chances of at least one TYPE I error among the three tests much greater than .05. It is like the increased probability of drawing an ace from a deck of cards when we can make multiple draws.
ANOVA allows us do an "overall" test of multiple groups to determine if there are any differences among groups within the set. Notice that ANOVA does not tell us which groups among the three groups are different from each other. The primary test ...
Assessment 4 ContextRecall that null hypothesis tests are of.docx
Assessment 4 Context
Recall that null hypothesis tests are of two types: (1) differences between group means and (2) association between variables. In both cases there is a null hypothesis and an alternative hypothesis. In the group means test, the null hypothesis is that the two groups have equal means, and the alternative hypothesis is that the two groups do not have equal means. In the association between variables type of test, the null hypothesis is that the correlation coefficient between the two variables is zero, and the alternative hypothesis is that the correlation coefficient is not zero.
Notice in each case that the hypotheses are mutually exclusive. If the null is false, the alternative must be true. The purpose of null hypothesis statistical tests is generally to show that the null has a low probability of being true (the p value is less than .05) – low enough that the researcher can legitimately claim it is false. The reason this is done is to support the allegation that the alternative hypothesis is true.
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6ONE-WAY BETWEEN-SUBJECTS ANALYSIS OFVARIANCE6.1 .docx
6
ONE-WAY BETWEEN-
SUBJECTS ANALYSIS OF
VARIANCE
6.1 Research Situations Where One-Way Between-Subjects
Analysis of Variance (ANOVA) Is Used
A one-way between-subjects (between-S) analysis of variance (ANOVA) is
used in research situations where the researcher wants to compare means on a
quantitative Y outcome variable across two or more groups. Group
membership is identified by each participant’s score on a categorical X
predictor variable. ANOVA is a generalization of the t test; a t test provides
information about the distance between the means on a quantitative outcome
variable for just two groups, whereas a one-way ANOVA compares means
on a quantitative variable across any number of groups. The categorical
predictor variable in an ANOVA may represent either naturally occurring
groups or groups formed by a researcher and then exposed to different
interventions. When the means of naturally occurring groups are compared
(e.g., a one-way ANOVA to compare mean scores on a self-report measure of
political conservatism across groups based on religious affiliation), the design
is nonexperimental. When the groups are formed by the researcher and the
researcher administers a different type or amount of treatment to each group
while controlling extraneous variables, the design is experimental.
The term between-S (like the term independent samples) tells us that each
participant is a member of one and only one group and that the members of
samples are not matched or paired. When the data for a study consist of
repeated measures or paired or matched samples, a repeated measures
ANOVA is required (see Chapter 22 for an introduction to the analysis of
repeated measures). If there is more than one categorical variable or factor
included in the study, factorial ANOVA is used (see Chapter 13). When there
is just a single factor, textbooks often name this single factor A, and if there
are additional factors, these are usually designated factors B, C, D, and so
forth. If scores on the dependent Y variable are in the form of rank or ordinal
data, or if the data seriously violate assumptions required for ANOVA, a
nonparametric alternative to ANOVA may be preferred.
In ANOVA, the categorical predictor variable is called a factor; the
groups are called the levels of this factor. In the hypothetical research
example introduced in Section 6.2, the factor is called “Types of Stress,” and
the levels of this factor are as follows: 1, no stress; 2, cognitive stress from a
mental arithmetic task; 3, stressful social role play; and 4, mock job
interview.
Comparisons among several group means could be made by calculating t
tests for each pairwise comparison among the means of these four treatment
groups. However, as described in Chapter 3, doing a large number of
significance tests leads to an inflated risk for Type I error. If a study includes
k groups, there are k(k – 1)/2 pairs of means; thus, for a set of four groups, the .
Introduction to ANOVA
In this ppt i have introduction, types of ANOVA, why to do ANOVA not multiple T-test, ANOVA assumptions, ANOVA examples
1917Repeated Measures Designs for Interval DataKaren K.docx
191
7Repeated Measures Designs for Interval Data
Karen Kasmauski/Corbis
Chapter Learning Objectives
After reading this chapter, you should be able to do the following:
1. Explain how initial between-groups differences affect t test or analysis of variance.
2. Compare the independent t test to the dependent-groups t test.
3. Complete a dependent-groups t test.
4. Explain what “power” means in statistical testing.
5. Compare the one-way ANOVA to the within-subjects F.
6. Complete a within-subjects F.
tan82773_07_ch07_191-226.indd 191 3/3/16 12:26 PM
© 2016 Bridgepoint Education, Inc. All rights reserved. Not for resale or redistribution.
Section 7.1 Reconsidering the t and F Ratios
Introduction
Tests of significant difference, such as the t test and analysis of variance, take two basic forms,
depending upon the independence of the groups. Up to this point, the text has focused only
on independent-groups tests: tests where those in one group cannot also be subjects in other
groups. However, dependent-groups procedures, in which the same group is used multiple
times, offer some advantages.
This chapter focuses on the dependent-groups equivalents of the independent t test and the
one-way ANOVA. Although they answer the same questions as their independent-groups
equivalents (are there significant differences between groups?), under particular circum-
stances these tests can do so more efficiently and with more statistical power.
7.1 Reconsidering the t and F Ratios
The scores produced in both the independent t and the one-way ANOVA are ratios. In the case
of the t test, the ratio is the result of dividing the difference between the means of the groups
by the standard error of the difference:
t 5
M1 2 M2
SEd
With ANOVA, the F ratio is the mean square between (MSbet) divided by the mean square
within (MSwith):
F 5
MSbet
MSwith
With either t or F, the denominator in the ratio reflects how much scores vary within (rather
than between) the groups of subjects involved in the study. These differences are easy to see
in the way the standard error of the difference is calculated for a t test. When group sizes are
equal, recall that the formula is
SEd 5 Î (SEM1)
2 1 (SEM2)
2
with
SEM 5
s
√n
and s, of course, a measure of score variation in any group.
So the standard error of the difference is based on the standard error of the mean, which in
turn is based on the standard deviation. Therefore, score variance within in a t test has its root
in the standard deviation for each group of scores. If we reverse the order and work from the
standard deviation back to the standard error of the difference, we note the following:
• When scores vary substantially in a group, the result is a large standard deviation.
• When the standard deviation is relatively large, the standard error of the mean must
likewise be large because the standard deviation is the numerator in the formula for SEM.
• A large standard erro.
INFERENTIAL TECHNIQUES. Inferential Stat. pt 3
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Research Methodology One-Way ANOVA - analysis of variance , pares the mean...
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Commonly used Statistics in Medical Research Handout
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Please help me to slove above integral. I little bit confused how to.pdf
Please help me to slove above integral. I little bit confused how to make it easy to integrate. Can
you please show me the working fully. tHank you
Solution
put lnx=y =>x=ey & dx=eydy... substitute these values in the integral and u get the answer.
Please help me solve questions below. Please give me crystal cle.pdf
Please help me solve questions below. Please give me crystal clear solutions.
I would like to see how to solve these problems a,b..
Solution
The method of moments estimator of λ = E(x)
E(x) = Σ x e^-λ λ^x / x!, x varies from 0 to infinity
= λ Σ e^-λ λ^(x-1) / (x-1)!, x from 1 to infinity
= λ
The first sample moment is xbar , the sample mean
Equating the sample and population means, the moment estimate of λ is xbar (the sample
mean).
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You can think of analysis of variance as a whole family of procedures beginning with the simple and frequently used t-test and becoming quite complicated with the use of multiple dependent variables (MANOVA, to be explained later in this chapter) and covariates.
Although the simpler varieties of these statistics can actually be calculated by hand, it is assumed that you will use a statistical software package for your calculations.
If you want to see how these calculations are done, you could try to compute a correlation, chi-square, t-test, or ANOVA yourself (see Yuker, 1958; Field, 2009), but in general it is too time consuming and too subject to human error to do these by hand.
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Several terms are used in these analyses that you need to be familiar with to understand the analyses themselves and the results. Many will already be familiar to you.
Statistical significance: This indicates the probability that the differences found are a result of error, not the treatment. Stated in terms of the P value, the convention is to accept either a 1% (P ≤ 0.01), or 1 out of 100, or 5% (P ≤ 0.05), or 5 out of 100, possibility that any differences seen could have been due to error (Cortina & Dunlap, 2007).
Research hypothesis: A research hypothesis is a declarative statement of the expected relationship between the dependent and independent variable(s).
Null hypothesis: The null hypothesis, based on the research hypothesis, states that the predicted relationships will not be found or that those found could have occurred by chance, meaning the difference will not be statistically significant.
Effect size: This is defined by Cortina and Dunlap as “the amount of variance in one variable accounted for by another in the sample at hand” (2007, p. 231). Effect size estimates are helpful adjuncts to significance testing. An important limitation, however, is that they are heavily influenced by the type of treatment or manipulation that occurred and the measures that are used.
Confidence intervals: Although sometimes suggested as an adjunct or replacement for the significance level, confidence intervals are determined in part by the alpha (significance level) (Cortina & Dunlap, 2007). Likened to a margin of error, the confidence intervals indicate the range within which the true difference between means may lie. A narrow confidence interval implies high precision; we can specify believable values within a narrow range ...
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ANOVA Parametric testAssessment 4 ContextRecall that null hypothesis tests are of.docx
Assessment 4 Context
Recall that null hypothesis tests are of two types: (1) differences between group means and (2) association between variables. In both cases there is a null hypothesis and an alternative hypothesis. In the group means test, the null hypothesis is that the two groups have equal means, and the alternative hypothesis is that the two groups do not have equal means. In the association between variables type of test, the null hypothesis is that the correlation coefficient between the two variables is zero, and the alternative hypothesis is that the correlation coefficient is not zero.
Notice in each case that the hypotheses are mutually exclusive. If the null is false, the alternative must be true. The purpose of null hypothesis statistical tests is generally to show that the null has a low probability of being true (the p value is less than .05) – low enough that the researcher can legitimately claim it is false. The reason this is done is to support the allegation that the alternative hypothesis is true.
In this context you will be studying the details of the first type of test again, with the added capability of comparing the means among more than two group at a time. This is the same type of test of difference between group means. In variations on this model, the groups can actually be the same people under different conditions. The main idea is that several group mean values are being compared. The groups each have an average score or mean on some variable. The null hypothesis is that the difference between all the group means is zero. The alternative hypothesis is that the difference between the means is not zero. Notice that if the null is false, the alternative must be true. It is first instructive to consider some of the details of groups.
One might ask why we would not use multiple t tests in this situation. For instance, with three groups, why would I not compare groups one and two with a t test, then compare groups one and three, and then compare groups two and three?
The answer can be found in our basic probability review. We are concerned with the probability of a TYPE I error (rejecting a true null hypothesis). We generally set an alpha level of .05, which is the probability of making a TYPE I error. Now consider what happens when we do three t tests. There is .05 probability of making a TYPE I error on the first test, .05 probability of the same error on the second test, and .05 probability on the third test. What happens is that these errors are essentially additive, in that the chances of at least one TYPE I error among the three tests much greater than .05. It is like the increased probability of drawing an ace from a deck of cards when we can make multiple draws.
ANOVA allows us do an "overall" test of multiple groups to determine if there are any differences among groups within the set. Notice that ANOVA does not tell us which groups among the three groups are different from each other. The primary test ...
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Recall that null hypothesis tests are of two types: (1) differences between group means and (2) association between variables. In both cases there is a null hypothesis and an alternative hypothesis. In the group means test, the null hypothesis is that the two groups have equal means, and the alternative hypothesis is that the two groups do not have equal means. In the association between variables type of test, the null hypothesis is that the correlation coefficient between the two variables is zero, and the alternative hypothesis is that the correlation coefficient is not zero.
Notice in each case that the hypotheses are mutually exclusive. If the null is false, the alternative must be true. The purpose of null hypothesis statistical tests is generally to show that the null has a low probability of being true (the p value is less than .05) – low enough that the researcher can legitimately claim it is false. The reason this is done is to support the allegation that the alternative hypothesis is true.
In this context you will be studying the details of the first type of test again, with the added capability of comparing the means among more than two group at a time. This is the same type of test of difference between group means. In variations on this model, the groups can actually be the same people under different conditions. The main idea is that several group mean values are being compared. The groups each have an average score or mean on some variable. The null hypothesis is that the difference between all the group means is zero. The alternative hypothesis is that the difference between the means is not zero. Notice that if the null is false, the alternative must be true. It is first instructive to consider some of the details of groups.
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The answer can be found in our basic probability review. We are concerned with the probability of a TYPE I error (rejecting a true null hypothesis). We generally set an alpha level of .05, which is the probability of making a TYPE I error. Now consider what happens when we do three t tests. There is .05 probability of making a TYPE I error on the first test, .05 probability of the same error on the second test, and .05 probability on the third test. What happens is that these errors are essentially additive, in that the chances of at least one TYPE I error among the three tests much greater than .05. It is like the increased probability of drawing an ace from a deck of cards when we can make multiple draws.
ANOVA allows us do an "overall" test of multiple groups to determine if there are any differences among groups within the set. Notice that ANOVA does not tell us which groups among the three groups are different from each other. The primary test.
Aca 22-407
T Test
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whether mean difference between two groups is statistically
significant.
6ONE-WAY BETWEEN-SUBJECTS ANALYSIS OFVARIANCE6.1 .docx
6
ONE-WAY BETWEEN-
SUBJECTS ANALYSIS OF
VARIANCE
6.1 Research Situations Where One-Way Between-Subjects
Analysis of Variance (ANOVA) Is Used
A one-way between-subjects (between-S) analysis of variance (ANOVA) is
used in research situations where the researcher wants to compare means on a
quantitative Y outcome variable across two or more groups. Group
membership is identified by each participant’s score on a categorical X
predictor variable. ANOVA is a generalization of the t test; a t test provides
information about the distance between the means on a quantitative outcome
variable for just two groups, whereas a one-way ANOVA compares means
on a quantitative variable across any number of groups. The categorical
predictor variable in an ANOVA may represent either naturally occurring
groups or groups formed by a researcher and then exposed to different
interventions. When the means of naturally occurring groups are compared
(e.g., a one-way ANOVA to compare mean scores on a self-report measure of
political conservatism across groups based on religious affiliation), the design
is nonexperimental. When the groups are formed by the researcher and the
researcher administers a different type or amount of treatment to each group
while controlling extraneous variables, the design is experimental.
The term between-S (like the term independent samples) tells us that each
participant is a member of one and only one group and that the members of
samples are not matched or paired. When the data for a study consist of
repeated measures or paired or matched samples, a repeated measures
ANOVA is required (see Chapter 22 for an introduction to the analysis of
repeated measures). If there is more than one categorical variable or factor
included in the study, factorial ANOVA is used (see Chapter 13). When there
is just a single factor, textbooks often name this single factor A, and if there
are additional factors, these are usually designated factors B, C, D, and so
forth. If scores on the dependent Y variable are in the form of rank or ordinal
data, or if the data seriously violate assumptions required for ANOVA, a
nonparametric alternative to ANOVA may be preferred.
In ANOVA, the categorical predictor variable is called a factor; the
groups are called the levels of this factor. In the hypothetical research
example introduced in Section 6.2, the factor is called “Types of Stress,” and
the levels of this factor are as follows: 1, no stress; 2, cognitive stress from a
mental arithmetic task; 3, stressful social role play; and 4, mock job
interview.
Comparisons among several group means could be made by calculating t
tests for each pairwise comparison among the means of these four treatment
groups. However, as described in Chapter 3, doing a large number of
significance tests leads to an inflated risk for Type I error. If a study includes
k groups, there are k(k – 1)/2 pairs of means; thus, for a set of four groups, the .
Introduction to ANOVA
In this ppt i have introduction, types of ANOVA, why to do ANOVA not multiple T-test, ANOVA assumptions, ANOVA examples
1917Repeated Measures Designs for Interval DataKaren K.docx
191
7Repeated Measures Designs for Interval Data
Karen Kasmauski/Corbis
Chapter Learning Objectives
After reading this chapter, you should be able to do the following:
1. Explain how initial between-groups differences affect t test or analysis of variance.
2. Compare the independent t test to the dependent-groups t test.
3. Complete a dependent-groups t test.
4. Explain what “power” means in statistical testing.
5. Compare the one-way ANOVA to the within-subjects F.
6. Complete a within-subjects F.
tan82773_07_ch07_191-226.indd 191 3/3/16 12:26 PM
© 2016 Bridgepoint Education, Inc. All rights reserved. Not for resale or redistribution.
Section 7.1 Reconsidering the t and F Ratios
Introduction
Tests of significant difference, such as the t test and analysis of variance, take two basic forms,
depending upon the independence of the groups. Up to this point, the text has focused only
on independent-groups tests: tests where those in one group cannot also be subjects in other
groups. However, dependent-groups procedures, in which the same group is used multiple
times, offer some advantages.
This chapter focuses on the dependent-groups equivalents of the independent t test and the
one-way ANOVA. Although they answer the same questions as their independent-groups
equivalents (are there significant differences between groups?), under particular circum-
stances these tests can do so more efficiently and with more statistical power.
7.1 Reconsidering the t and F Ratios
The scores produced in both the independent t and the one-way ANOVA are ratios. In the case
of the t test, the ratio is the result of dividing the difference between the means of the groups
by the standard error of the difference:
t 5
M1 2 M2
SEd
With ANOVA, the F ratio is the mean square between (MSbet) divided by the mean square
within (MSwith):
F 5
MSbet
MSwith
With either t or F, the denominator in the ratio reflects how much scores vary within (rather
than between) the groups of subjects involved in the study. These differences are easy to see
in the way the standard error of the difference is calculated for a t test. When group sizes are
equal, recall that the formula is
SEd 5 Î (SEM1)
2 1 (SEM2)
2
with
SEM 5
s
√n
and s, of course, a measure of score variation in any group.
So the standard error of the difference is based on the standard error of the mean, which in
turn is based on the standard deviation. Therefore, score variance within in a t test has its root
in the standard deviation for each group of scores. If we reverse the order and work from the
standard deviation back to the standard error of the difference, we note the following:
• When scores vary substantially in a group, the result is a large standard deviation.
• When the standard deviation is relatively large, the standard error of the mean must
likewise be large because the standard deviation is the numerator in the formula for SEM.
• A large standard erro.
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Research Methodology One-Way ANOVA - analysis of variance , pares the mean...
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Commonly used Statistics in Medical Research Handout
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Please help me to slove above integral. I little bit confused how to.pdf
Please help me to slove above integral. I little bit confused how to make it easy to integrate. Can
you please show me the working fully. tHank you
Solution
put lnx=y =>x=ey & dx=eydy... substitute these values in the integral and u get the answer.
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Please help me solve questions below. Please give me crystal clear solutions.
I would like to see how to solve these problems a,b..
Solution
The method of moments estimator of λ = E(x)
E(x) = Σ x e^-λ λ^x / x!, x varies from 0 to infinity
= λ Σ e^-λ λ^(x-1) / (x-1)!, x from 1 to infinity
= λ
The first sample moment is xbar , the sample mean
Equating the sample and population means, the moment estimate of λ is xbar (the sample
mean).
Please help with the following practice questionsQuesti.pdf
Please help with the following practice questions:
Question 1
F can never be negative. a.
True. b.
False.
Question 2
If the HSD is 22, a paired mean difference of 23 would indicate that that pair contributed toward
the significant F. a.
True. b.
False.
Question 3
One drawback of ANOVA is that it allows type 1 error to escalate for every additional group
tested. Answer a.
True. b.
False.
Question 4
The sum of within-groups variance and between-group variance is the total variance. a.
True. b.
False.
Question 5
An ANOVA with one independent variable but four groups is known as a one-way ANOVA.
a.
True. b.
False.
Question 6
In ANOVA, where should you look for the treatment effect? a.
In the within-group variance. b.
In the between-group variance. c.
In the total variance. d.
Within a single individual\'s score.
Question 7
When the null hypothesis is true, the calculated F should be close to a.
0. b.
1. c.
Infinity. d.
There is not enough information to tell.
Question 8
The appropriate statistic to use when testing the hypothesis for a study with three treatment
groups is a a.
One-sample t test. b.
Two-sample t test. c.
ANOVA F test. d.
Either b or c is appropriate.
Question 9
The Tukey HSD is the a.
Smallest amount by which any two means must differ in order to be statistically significant.
b.
Smallest amount by which any two means actually do differ. c.
Largest amount by which any two means actually do differ. d.
Largest amount by which any two means can possibly differ.
Question 10
When and why would you conduct post hoc tests as a follow up to the overall F test? a.
When the overall F test is NOT significant; to determine which of the pairs of groups may be
significant. b.
When the overall F test is NOT significant; to determine why the overall F test is not significant.
c.
When the overall F test is significant; to determine between which pairs of groups the significant
difference lies. d.
When the overall F test IS significant; to determine if the significance is only the result of
chance.
Please help with the following practice questions:
Question 1
F can never be negative. a.
True. b.
False.
F can never be negative. a.
True. b.
False.
F can never be negative. a.
True. b.
False.
F can never be negative.
F can never be negative.
True.
False.
Question 2
If the HSD is 22, a paired mean difference of 23 would indicate that that pair contributed toward
the significant F. a.
True. b.
False.
If the HSD is 22, a paired mean difference of 23 would indicate that that pair contributed toward
the significant F. a.
True. b.
False.
If the HSD is 22, a paired mean difference of 23 would indicate that that pair contributed toward
the significant F. a.
True. b.
False.
If the HSD is 22, a paired mean difference of 23 would indicate that that pair contributed toward
the significant F.
If the HSD is 22, a paired mean difference of 23 would indicate that that pair contributed toward
the significant F.
True.
False.
Question 3
One drawback of ANOVA is that it al.
Please help me with my Assignment! Find the general solution of(.pdf
Please help me with my Assignment!
Find the general solution of
(d4y / dx4) + 4(d3y / dx3) + 8(d2y / dx2) + 8(dy/dx) + 4y = 0
Solution
The characteristic equation of the DE is:
r4 + 4r3 + 8r2 + 8r + 4 = 0
(r2 + 2r + 2)2 = 0
So the roots are:
-1-i , -1-i , -1+i , -1+i
Therefore the general solution is:
y = Ae-xcos(x) + Be-xsin(x) + C.xe-xcos(x) + D.xe-xsin(x)
Where A, B, C, and D are constants..
Please Help The content of magnesium in an alloy is a random variabl.pdf
Please Help The content of magnesium in an alloy is a random variable given by the following
probability density function: The profit form this alloy is P = 10 + 4X Find the probability
distribution of P. What is the expected profit E[P].
Solution
b) E[P]=10+4E[X]
=10+4 int from lim 0-sqrt 20(x* x/10)dx
=10+4[(1/10)*(x^3/3)]lim 0-sqrt 20
=10+4(20sqrt20/30-0/30)
=10+4(2.98)
=10+11.92
=21.92.
Please help me with this. I have the answer as you can see. I need t.pdf
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it step by step. I cannot get it. I basically do not know the inbetween steps from the second line
and how it leads to the answer.RS = R0 + (B / S)(R0RB).1305 = R0 + (.30)(R0 .05)R0 =
.1117, or 11.17%
Solution
RS = R0 +(B/S)(R0-RB)
0.1305=R0+(0.30)(R0-0.05)
0.1305=R0+R0(0.30)-(0.30)(0.05)
0.1305=R0+R0(0.30)-0.015
0.1305+0.015=R0+R0(0.30)
0.1455=R0(1+0.30)
0.1455=R0(1.30)
0.1455/1.30=R0
R0=0.111923
R0=11.19%.
Please help Prove that the following functions are cumulative distri.pdf
Please help Prove that the following functions are cumulative distribution functions. 1/2+1pi
tan-1(x),x (-infinity, +infinity) 1-e -x, x (0,+infinity).
Solution
(a)when x->inf,,F(x)->1/2 + 1/pi (pi/2)=1/2 +1/2 =1
when x->-inf F(x)->1/2 - 1/2 =0.
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yr8! how do i prove that a x-1 enlargement is the same as the original shape using thses words...
bisect, parrallel, congruent (words like that) HELP
Solution
RESIPROCALL.
Please help me and explain 1. If all other agents choose a level of .pdf
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can be expressed as V(ei, e). In this case the optimization problem becomes max ei V(ei, e),
Using the optimization problem, show the the conditions for coordination failures. And also
discuss coordination failures with help of the game theoretical structures such as the battle of the
sexes, prisoners\' dilemma and the stag hunt bu giving the real cases in the economy.
Solution
Problem of coordination in a shared production process. Let ej be the effort (input) of
player i in the production of a public good, c. The production
function is c = f(ei,e) with fi > 0 and f2> 0. Agents have identical
utility functions defined over consumption and effort U(c,e), with
U1 > 0, U2 < 0, and U() quasi-concave. These preferences, together
with the production function, generate agent i\'s payoff function:
(1) V(ei,e) = U(f(ej,e),ej.
Note that V12= U1f2> 0, so the model exhibits positive
spillovers.
Differentiating the payoff function with respect to ei and e yields
(2) V12 = U1f12 + U1lflf2 + U12f2.
that preferences between consumption and effort are separable. A
necessary condition for strategic complementarity is then that
inputs be complementary within the production process (/12 > 0).
Increases in e will induce increases in ej if /12 is large enough to offset
the reduction in the marginal utility of consumption brought about
by f2 > 0. If U() is linear, /12 > 0 is sufficient for strategic
complementarities.
Note further that
- V12 UJ1/2 + Ullfl2
V1l -[U11 (fl)2 + U1f/1 + U22]
The necessary condition for multiplicity (p > 1) is
(4) U1(/12 + /11) + U1l[(/l)2 + /1/2] _ -U22.
Multiple Equilbria are thus more likely when the utility function is not very concave with
respect to consumption and effort; when inputs are highly complementary in production; and
when the production function is not very concave with respect to own effort..
Please help verify the identity showing the steps.1(sec-tan) = .pdf
Please help verify the identity showing the steps.
1/(sec?-tan?) = sec? + tan?
Thank you!
Solution
LHS: 1/(sect -tant)
Multiply sect +tant in both denominator and numerator.
= (sect +tant)/[(sect +tant)(sect -tant)]
= (sect +tant)/(sec^2t -tan^2t)
= (sect +tant)/1
= sect + tant ---> RHS.
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Please help me aswer this...I will rate you life saving!!!
Solution
Oral cavity Pharynx Esophagus Stomach Duodenum Jejunum Ileum Cecum Ascending Colon
Transverse Colon DescendingColon Sigmoid Colon Rectum Anus Feces Human waste
Duodenum to the Ileum that isfrom the small intestine
Cecum to Rectum is from thelarge intestine
Duodenum to the Ileum that isfrom the small intestine
Cecum to Rectum is from thelarge intestine.
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Please give step by step and explain to get A+ rating
Solution
int f(x) dx (x from 0 to ) = 1
Thus
int k.e-x/2 dx (x from 0 to ) = 1
-2k.e-x/2 (x from 0 to ) = 1
-2ke- - (-2ke0) = 1
0 + 2k = 1
k = 1/2
P(X >= 1) = int f(x) dx (x from 1 to ) =
int 1/2 e-x/2 dx (x from 1 to ) =
-e-x/2 (x from 1 to ) =
-e- - (-e-1/2) = 0 + e-1/2 = e-1/2.
Please help Lot, Xn and Yn have the distributions shown above. Fi.pdf
Please help
Lot, Xn and Yn have the distributions shown above. Find the expected value and variance of
Xn and Yn. What does the Chebyshev inequality\'- tell us about the convergence of Xn and Yn?
Is Yn convergent in probability? If so, to what value?
Solution
1. E(X) = 0*1/n + 1*1/n = 1/n ; V(X) = E(X^2) - (E(X))^2 = (0*1/n + 1*1/n) - (1/n)^2 = 1/n -
1/n^2 = (n-1)/(n^2)
E(Y) = 0*1/n + n*1/n = 1 ; V(Y) = E(Y^2) - (E(Y))^2 = (0*1/n + n^2/n) - (1) = n-1
2.Pr(|X-1/n|>k*sqrt(V(X))) <= 1/(k^2) for k>=1
Pr(|Y-1|>k*sqrt(V(Y))) <= 1/(k^2) for k>=1
3. No its not convergent as Sum of P(X) not converging to 1.
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Please help me and explain
Graphically show and verbally argue that a Walrasian equiblirium in an economy is Pareto
efficient as long as preferences are locally non-satiated. How about convexity? ?s it important for
this theorem to hold ?
Solution
Walras Equil.
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Event Link:-
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● Java 17 Upgrade Overview
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● Which MuleSoft products are in scope?
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YouTube:- youtube.com/@mulesoftmysore
Mysore WhatsApp group:- https://chat.whatsapp.com/EhqtHtCC75vCAX7gaO842N
Speaker:-
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Priya Shaw - https://www.linkedin.com/in/priya-shaw
Organizers:-
Shubham Chaurasia - https://www.linkedin.com/in/shubhamchaurasia1/
Giridhar Meka - https://www.linkedin.com/in/giridharmeka
Priya Shaw - https://www.linkedin.com/in/priya-shaw
Shyam Raj Prasad-
https://www.linkedin.com/in/shyam-raj-prasad/
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This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
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Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Introduction to AI for Nonprofits with Tapp Network
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
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Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
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How libraries can support authors with open access requirements for UKRI funded books
Wednesday 22 May 2024, 14:00-15:00.
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The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
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Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Embracing GenAI - A Strategic Imperative
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdf
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
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Presentation required for the master in Education.
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