Dissertation help provides help to students who face challenges in submitting their dissertation. Dissertation Help is very useful to students in all fields, most of whom are stuck in their dissertation work. Dissertation help provides statistical help in various fields, like business, psychology, medicine, etc.
Let us discuss in detail how Dissertation Help provides statistical guidance in such fields.
In the field of business, Dissertation Help provides immense help in the field of market research. Dissertation Help provides detailed case studies on how market research is carried out on a particular product. Dissertation Help provides information about the methods by which data is to be retrieved. Dissertation Help also provides information on how a questionnaire is to be prepared in order to get valid data. Dissertation Help provides information on the various statistical techniques used during market research. Dissertation Help provides information about the correlation between good data and a valid inference after the analysis. Dissertation Help is generally provided by professors who have attained their doctorates in the statistics field. It is also provided by other statistical consultants.
Dissertation Help guides people on financial modeling. Dissertation Help, in this case, is generally provided by some expert financial analysts. Dissertation Help gives information on how to write a report, which can describe an opinion on a company’s investment potential. Dissertation Help provides information on various financial models, like Discounted Cash Flow model, Binomial Pricing Model, etc.
In the field of medicine, Dissertation Help provides immense help as it gives information on the analytical techniques being used. These analytical techniques include Meta analysis while performing clinical trials on a particular drug. Dissertation Help provides information about various statistical operations on pre-clinical programs, drug production, launch management, contract research, manufacturing management, drug process development, optimization, regulatory and quality management, validation of the drug, package development, line integration of the drug, and manufacturing engineering of the drug. Dissertation Help provides information about the phases that a drug undergoes during clinical trials. Dissertation Help also provides knowledge about survival analysis, which helps in knowing that a fraction of the population would have survived in the past at a certain point of time. Dissertation Help gives a mathematical interpretation of this technique, which says that the probability of a person dying at time ‘T’ is much later than the specified time ‘t.’ Dissertation Help also provides information about the assumptions of survival analysis, which approaches zero as age increases.
In the field of psychology, Dissertation Help provides information about two types of statistical distributions, which are continuous statistical distribution and discrete statistical distribution. Dissertation Help provides information about the major difference between these statistical distributions. Dissertation Help provides information that discrete distribution is designated as probability mass function (pmf) and continuous distribution is designated as probability density function (pdf). Dissertation Help provides information about which samples are countable (for example, the number of bulbs) and fall under discreet distribution, and information about which samples cannot be counted (for example, the intensity of power) and fall under continuous distribution. Dissertation Help provides information about various distributions falling under discreet statistical distribution like Poisson distribution, Binomial distribution, Bernoulli distribution, etc. Dissertation Help provides information about various distributions falling under continuous statistical distribution, like uniform distribution, hyper geometric distribution, etc.
Dissertation Help makes sincere efforts in making student’s work better. But Dissertation Help should not be misinterpreted as a medium by which students do not need to work after submitting their work to Dissertation Help. On the contrary, students should also work hard and make sincere efforts as they receive Dissertation Help.
Wednesday, April 29, 2009
Monday, April 27, 2009
Sample Size
Sample size plays a very crucial role in conducting statistical tests and analyses in various fields, such as business, medicine, nursing, psychology, etc. Sample size is correlated to making an appropriate decision or inference about the product from which the sample has been drawn. In other words, if the sample size is too small, then a systematically conducted study can fail to detect the important effects, associations or correlations. In the same manner, if the sample size is too large, then the study would be complex and tedious. Thus, an optimum sample size is the most important part in any statistical study. This document, therefore, will detail the role played by sample size in business, medicine, nursing, and psychology fields.
In the field of psychology, if one has to compare the difference of means of two populations with a particular sample size, or has to test for a single mean with some sample size, or has to compare the means from two different populations with some sample size using the t-test, then the sample size should be less than 30. If one has to compare the differences of means of two populations with some sample size, or has to test for a single mean with some sample size, or has to compare the means from two different populations with some sample size using the Z- test, then the sample size should be greater than 30. If one has to perform regression analysis in order to predict the attitude of teenagers, generally the sample size should be 10 for each independent variable, defined during that analysis. This means that if there are two independent variables in that study, then the sample size should be at least 20. If, in this study, the data is of categorical type, then the sample size should be more in order to perform the same analysis. In the case of focus group studies, the sample size should be around two from the population and it should have 6-15 groups.
In the field of business, for example in the case of market research studies, the sample size varies from study to study. If one is performing a problem- identification market research study, then the minimum sample size should be around 500 from a population of the size of 1000-2500. If one is performing a problem-solving market research study, then the sample size should be around 200 from a population of the size of 300-500. If one is performing a TV / radio advertising market research study, then the minimum sample size should be around 150, from a population of the size of 200-300. If one wants to test market audits, then the sample size of the stores should be around 10 from a population of 20 stores.
In the field of medicine / nursing, getting a correct sample size is very important. An example can clearly show just how important sample size is in research. In this example, if a researcher wants to know whether or not there is any difference in the curability rate of drug A and drug B, a sample size of 150 patients must be applied to test the two drugs. If in this example there is no difference observed between the two drugs, then a sample size of 150 patients could be the problem. This would be known as a Type II error and this kind of error is very dangerous.
Thus, a larger sample size will avoid this serious error. Sample size, then, plays a crucial role as it is used to avoid Type II errors.
Generally, one should always keep in mind that if the population size is smaller, then one should have a bigger sample size and if the population is large, then one should have a smaller sample size.
For more information on calculating sample size, click here.
In the field of psychology, if one has to compare the difference of means of two populations with a particular sample size, or has to test for a single mean with some sample size, or has to compare the means from two different populations with some sample size using the t-test, then the sample size should be less than 30. If one has to compare the differences of means of two populations with some sample size, or has to test for a single mean with some sample size, or has to compare the means from two different populations with some sample size using the Z- test, then the sample size should be greater than 30. If one has to perform regression analysis in order to predict the attitude of teenagers, generally the sample size should be 10 for each independent variable, defined during that analysis. This means that if there are two independent variables in that study, then the sample size should be at least 20. If, in this study, the data is of categorical type, then the sample size should be more in order to perform the same analysis. In the case of focus group studies, the sample size should be around two from the population and it should have 6-15 groups.
In the field of business, for example in the case of market research studies, the sample size varies from study to study. If one is performing a problem- identification market research study, then the minimum sample size should be around 500 from a population of the size of 1000-2500. If one is performing a problem-solving market research study, then the sample size should be around 200 from a population of the size of 300-500. If one is performing a TV / radio advertising market research study, then the minimum sample size should be around 150, from a population of the size of 200-300. If one wants to test market audits, then the sample size of the stores should be around 10 from a population of 20 stores.
In the field of medicine / nursing, getting a correct sample size is very important. An example can clearly show just how important sample size is in research. In this example, if a researcher wants to know whether or not there is any difference in the curability rate of drug A and drug B, a sample size of 150 patients must be applied to test the two drugs. If in this example there is no difference observed between the two drugs, then a sample size of 150 patients could be the problem. This would be known as a Type II error and this kind of error is very dangerous.
Thus, a larger sample size will avoid this serious error. Sample size, then, plays a crucial role as it is used to avoid Type II errors.
Generally, one should always keep in mind that if the population size is smaller, then one should have a bigger sample size and if the population is large, then one should have a smaller sample size.
For more information on calculating sample size, click here.
Thursday, April 23, 2009
Sample Size Justification
Sample size justification deals with the justification of the sample size. Sample size justification is very important because it affects the results of the research. If, for example, the sample size is too small, then even if everything else has been carried out perfectly, the inferences drawn will not be valid or perfect. On the other hand, sample size justification is also important in the case of a sample size being too large. In this case, if the sample size is too large, the results can also provide a false statistical inference. Therefore, sample size justification is important in order to make a valid inference about the product being tested. Sample size justification is important in the field of psychology, business and medicine and nursing.
In the field of psychology, sample size justification is important if one is trying to compute the difference of means from two populations using a t-test. Sample size justification implies that in this case, the sample size should be less than 30. Sample size justification is also important if one is trying to compute the difference of means from two populations using the z test. Sample size justification implies that in this case, the sample size should be more than 30. Sample size justification is also important in the case of regression analysis. Sample size justification is important if, for example, one is trying to predict the behavior of a child in his teenage years given some other dependent variables. Sample size justification implies that in regression analysis, there should be at least 10 samples for each independent variable.
In the field of business, sample size justification plays a crucial role in the case of market research study. Sample size justification is important in this case, when one is performing a problem of identification in market research study. Sample size justification implies that the minimum sample size should be around 500 from a population of the size of 1000-2500. Sample size justification is also important when one is performing problem solving in market research study. Sample size justification implies that the sample size should be around 200 from a population of the size of 300-500. For example, sample size justification is important, let’s say, if one is performing TV / radio advertising market research study. Sample size justification implies that the minimum sample size should be around 150 from a population of the size of 200-300. Sample size justification is equally important if one wants to test market audits. Sample size justification implies that the sample size of the stores should be around 10 from a population of 20 stores.
In the field of medicine / nursing, sample size justification is important when a researcher wants the curability effect of two drugs, say, drug A and drug B. Sample size justification implies that this test should be carried out on more than 150 patients, otherwise this may result in a Type II error. Thus, sample size justification can prevent a researcher from getting a Type II error, which is the most serious error in the field. If a researcher goes against the sample size justification in this case, i.e. by not conducting the test on 150 patients, then the result will say that there is no difference in drug A and drug B—and this is a serious error called Type II error. Thus, the researcher must deal properly with sample size justification.
It is important for a researcher to always keep sample size justification in mind. The researcher must always be aware of sample size justification, otherwise the results of their research will not be valid.
In the field of psychology, sample size justification is important if one is trying to compute the difference of means from two populations using a t-test. Sample size justification implies that in this case, the sample size should be less than 30. Sample size justification is also important if one is trying to compute the difference of means from two populations using the z test. Sample size justification implies that in this case, the sample size should be more than 30. Sample size justification is also important in the case of regression analysis. Sample size justification is important if, for example, one is trying to predict the behavior of a child in his teenage years given some other dependent variables. Sample size justification implies that in regression analysis, there should be at least 10 samples for each independent variable.
In the field of business, sample size justification plays a crucial role in the case of market research study. Sample size justification is important in this case, when one is performing a problem of identification in market research study. Sample size justification implies that the minimum sample size should be around 500 from a population of the size of 1000-2500. Sample size justification is also important when one is performing problem solving in market research study. Sample size justification implies that the sample size should be around 200 from a population of the size of 300-500. For example, sample size justification is important, let’s say, if one is performing TV / radio advertising market research study. Sample size justification implies that the minimum sample size should be around 150 from a population of the size of 200-300. Sample size justification is equally important if one wants to test market audits. Sample size justification implies that the sample size of the stores should be around 10 from a population of 20 stores.
In the field of medicine / nursing, sample size justification is important when a researcher wants the curability effect of two drugs, say, drug A and drug B. Sample size justification implies that this test should be carried out on more than 150 patients, otherwise this may result in a Type II error. Thus, sample size justification can prevent a researcher from getting a Type II error, which is the most serious error in the field. If a researcher goes against the sample size justification in this case, i.e. by not conducting the test on 150 patients, then the result will say that there is no difference in drug A and drug B—and this is a serious error called Type II error. Thus, the researcher must deal properly with sample size justification.
It is important for a researcher to always keep sample size justification in mind. The researcher must always be aware of sample size justification, otherwise the results of their research will not be valid.
Tuesday, April 7, 2009
Sample Size Calculation
Sample size calculation plays a very important role in statistics analysis. Sample size calculation refers to how much data we need for particular research to make a correct decision. If we have more data, then our decision will be more accurate, and there will be less error of the parameter estimate. Some of the factors that affect the sample size calculation are the type of data, including the Power of the sample size, the Technique used for analysis, the Marginal error, the Level of Significance, the Standard Deviation, the Missing value, etc. First of all, we should consider the type of data level and the measurement of a specific sample size.
There are four types of data level:
(1) nominal data
(2) ordinal data
(3) interval data
(4) ratio data
Nominal data is simply categorical data. Ordinal data is the data when ranks are assigned to the data. Interval data is the data when an interval is given between the cases. Ratio data is metric or continuous data, on which we can perform all analysis which we cannot perform on nominal, ordinal and interval data. In determining the sample size calculation, we should consider the level of significance or the level of alpha. For instance, two tailed test alpha level is 5%, which is equal to 1.96. When the sample size calculation is done, we should consider the marginal error as well. Marginal error is simply the error that a researcher is willing to accept for a particular sample size. For example, in continuous data t value of alpha for 5% is 1.96 and SD in population is 1.167, and marginal error is .21. Then we can calculate the sample size by using the following formula:
N=
There are four types of data level:
(1) nominal data
(2) ordinal data
(3) interval data
(4) ratio data
Nominal data is simply categorical data. Ordinal data is the data when ranks are assigned to the data. Interval data is the data when an interval is given between the cases. Ratio data is metric or continuous data, on which we can perform all analysis which we cannot perform on nominal, ordinal and interval data. In determining the sample size calculation, we should consider the level of significance or the level of alpha. For instance, two tailed test alpha level is 5%, which is equal to 1.96. When the sample size calculation is done, we should consider the marginal error as well. Marginal error is simply the error that a researcher is willing to accept for a particular sample size. For example, in continuous data t value of alpha for 5% is 1.96 and SD in population is 1.167, and marginal error is .21. Then we can calculate the sample size by using the following formula:
N=
N= sample size
t= level of alpha
S= standard deviation
D= marginal error
When the data is categorical, then we can use the probability of method instead of the standard deviation. For example, when we have two categories for samples, then we can use .5 probability of the first category and .5 probability for the second category. We can use the following formula in the case of categorical data:
N=
N= sample size
t= level of alpha
P= probability of event happening
Q= probability of second event happening
D= marginal error
These are some basic formulas for sample size calculation. But sample size calculation differs from technique to technique. For example, when we are comparing the means of two populations, if the sample size is less than 30, then we will use the t-test. If the sample size is greater than 30, then we will use the Z-test for comparing the two populations’ sample means. As a rule of thumb, in regression analysis, there should be 10 cases for each independent variable. For example, if we have two independent variables, then the minimum sample size should be 20 in order to reach a correct decision about the regression parameter. If the sample size is less than the given criteria, then the decision will not be correct. When data are categorical and the level of alpha decreases, then the sample size should be bigger for the same technique. If the population size is smaller, then we need a bigger sample size, and if the population is large, then we need a smaller sample size as compared to the small population. Sample size will differ with different margin error. Missing value also affects the sample size. When data has missing value, we need bigger sample sizes as compared to the non-missing value sample. In an analysis of variance test, we need to determine how many covariates we can use for a particular treatment variable. We can use sample size to determine the covariate. For example, with a sample size of 50, and a number of groups in 3 treatment factor, we can use only 3 covariates in an analysis of variance study. Variance also affects the sample size. When variance is more for the variable taken into study, then the sample size needs to be bigger to reach a correct decision about the parameter estimate. When we do not know the population standard deviation, then we can use the range to know the standard deviation. Sample size also depends on the power. With more power, we need a bigger sample size.
For information on statistical consulting services, click here.
t= level of alpha
P= probability of event happening
Q= probability of second event happening
D= marginal error
These are some basic formulas for sample size calculation. But sample size calculation differs from technique to technique. For example, when we are comparing the means of two populations, if the sample size is less than 30, then we will use the t-test. If the sample size is greater than 30, then we will use the Z-test for comparing the two populations’ sample means. As a rule of thumb, in regression analysis, there should be 10 cases for each independent variable. For example, if we have two independent variables, then the minimum sample size should be 20 in order to reach a correct decision about the regression parameter. If the sample size is less than the given criteria, then the decision will not be correct. When data are categorical and the level of alpha decreases, then the sample size should be bigger for the same technique. If the population size is smaller, then we need a bigger sample size, and if the population is large, then we need a smaller sample size as compared to the small population. Sample size will differ with different margin error. Missing value also affects the sample size. When data has missing value, we need bigger sample sizes as compared to the non-missing value sample. In an analysis of variance test, we need to determine how many covariates we can use for a particular treatment variable. We can use sample size to determine the covariate. For example, with a sample size of 50, and a number of groups in 3 treatment factor, we can use only 3 covariates in an analysis of variance study. Variance also affects the sample size. When variance is more for the variable taken into study, then the sample size needs to be bigger to reach a correct decision about the parameter estimate. When we do not know the population standard deviation, then we can use the range to know the standard deviation. Sample size also depends on the power. With more power, we need a bigger sample size.
For information on statistical consulting services, click here.
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