Soru arşivi yükleniyor…
Sorular yükleniyor…
Sınav ve konu sorularıyla çalış. Cevabını kontrol et, zorlandığın soruları kaydet ve yeniden çöz.
111 sorunun metninde arama yapabilirsin.
Diğer 3 görselli soruları kendi sayfalarında çözebilirsin.
Kendi cevabını düşün, sonra yayımlanmış cevapla karşılaştır. Değerlendirmeyi sen yaparsın.
İlerleme yükleniyor…
H0: µ1 – µ2 = 0 H1: µ1 – µ2 > 0 or H0: µ1 = µ2 H1: µ1 > µ2
Cevabın yayımlanmış cevapla uyuştu mu?
z test statistic can be used for difference between two means if sample sizes are greater than or equal to 30. In many real-life circumstances, it’s not possible or practical to attain the sample size of 30 or more due to the time, research budget and other relevant constraints. Also, usually the population standard deviations are unknown. Under these circumstances, t test is used to test the difference between means. At this time also, the assumption regarding the population distribution of the variables should be normal or approximately normal, and two samples are independently selected from these populations.
Cevabın yayımlanmış cevapla uyuştu mu?
For the right sided (upper tailed) test, the null hypothesis (H0) and the alternative hypothesis (H1) are in the following form, H0: p1 = p2 H1: p1 > p2
Cevabın yayımlanmış cevapla uyuştu mu?
For the left sided (lower tailed) test, the null hypothesis (H0) and the alternative hypothesis (H1) are in the following form, H0: p1 = p2 H1: p1 < p2
Cevabın yayımlanmış cevapla uyuştu mu?
The null hypothesis and alternative hypothesis for one-way analysis of variance test can be defined as follows, H0: µ1 = µ2 = µ3 = ..... = µk (all population means under consideration are equal) H1: At least one of population means is different µ1, µ2, µ3, ..... , µk.
Cevabın yayımlanmış cevapla uyuştu mu?
rejecting the null hypothesis in an ANOVA test means that at least one of the means of the population under consideration is different from the other population's means. To determine which of the population mean(s) are different we need additional statistical tests. F test is used if we consider more than two means of population. By the help of the F test, all the population means are compared simultaneously. Otherwise, if you consider comparing two means at a time as we considered in the previous sections, the number of tests increases enormously as the number of populations and their means increase. For example, to compare three population's means at a time, we need three t-tests. In a similar manner, to compare five population's means at a time, we need 10 t-tests.
Cevabın yayımlanmış cevapla uyuştu mu?
To utilize the one-way ANOVA test, the samples must be independent and randomly selected from a normal or approximately normal population. Also, each population variance under consideration must be equal.
Cevabın yayımlanmış cevapla uyuştu mu?
For the one-way ANOVA test, the test statistic is the ratio of two variances: namely the variance between samples and the variance within samples. These variances constitute the estimates of the population variance. The variance between samples are also called as between group variance, and the variance within samples are also called as within group variance. The variance between samples measures the differences associated with the treatment which is given to each sample and abbreviated as MSB . On the other hand, the variance within samples measures the differences associated with observations within the same sample abbreviated as MSW . The test statistic for a one-way ANOVA test is the ratio of two variances; namely the variance between samples (MSB) and variance within samples (MSW).
Cevabın yayımlanmış cevapla uyuştu mu?
The F (one-way ANOVA) test to compare more than two population means is always a right tailed test. Therefore, H0 will be rejected if the test statistic F is greater than the critical value. From the F test statistic, it’s clear that the value of the test statistic is close to the 1 if MSB close MSW , means that there is little or no difference between the means. Then, the F test statistic value nearby to 1 suggests that we fail to reject the null hypothesis (H0). If F test statistic value is greater than 1 we suggest that the null hypothesis should be rejected. This situation occurs when one population mean is significantly different than the other population's means, and this state is revealed if MSB is greater than MSW .
Cevabın yayımlanmış cevapla uyuştu mu?
In order to look at the relationship between two variables, a scatterplot of the data points can be drawn. In scatterplot, all pairwise values of the two continuous variables are plotted in two dimensions.
Cevabın yayımlanmış cevapla uyuştu mu?
We should caution the reader not to confuse correlation with causation. Correlation and causation are two different things. If we find a correlation betwen two variables it does not mean that one variable causes the other variable. The study of causation is totaly different from correlation.
Cevabın yayımlanmış cevapla uyuştu mu?
The Pearson’s correlation coefficient is a very useful tool to show the degree of the linear relationship between two variables. The Pearson’s correlation coefficient is not a perfect tool, and we should be careful while using it. First of all, Pearson’s correlation coefficient does not show the causal relationship. A relationship shown by Pearson’s correlation coefficient does not necessarily mean that one variable’s existence is the cause of the second variable’s existence. There may always many known or unknown effects on the existence of a variable.
Cevabın yayımlanmış cevapla uyuştu mu?
Pearson’s correlation coefficient is easily affected by the outliers. In order to control this problem, careful examination inspection of the relationship with a scatterplot will be very helpful. If a data pair is identified as an outlier or as unusual observations, the researcher may investigate the reasons of the existence of these values in such a problem and may or may not include these data points in his/her investigation.
Cevabın yayımlanmış cevapla uyuştu mu?
In correlation and regression problems, the coefficient of determination measures the proportion of the variance of dependent variable (y), given by independent variable (x), when y is expressed as a linear regression on x. In brief, the coefficient of determination shows the amount of the variability of dependent variable (y) is explained by the independent variable (x). There may be more than one independent variable to explain the total variability of dependent variable y. It is not feasible to think that one variable will explain all the variability in dependent variable. The coefficient of determination is calculated by using the square of Pearson’s correlation coefficient, r2. Remember Pearson’s correlation coefficient takes values between -1 and +1, therefore it is easy to see that the coefficient of determination takes values between 0 and +1. For example, let’s say that Pearson’s correlation coefficient is -0.48 in a problem. Therefore, we know that there is a negative moderate correlation between the variables. The coefficient of determination in the same problem is r2= (-0.48)2 = 0.23. The practical meaning of this result can be written as follows: “the independent variable in this problem explains the 23% of the variability of the dependent variable”. If we consider the total variability of dependent variable as 100%, it means that there is still 77% of unaccounted variability for the dependent variable. There must be some other effects on the variability of the dependent variable.
Cevabın yayımlanmış cevapla uyuştu mu?
In order to estimate the parameter on regression model, we may use a method called the least squares. The least squares method essentially minimizes the sum of the squares of the residuals, the difference between an observed value and the value obtained from the proposed model for that object.
Cevabın yayımlanmış cevapla uyuştu mu?
In regression analysis problems, a scatterplot is always helpful. Remember that by looking at the scatterplot it is possible to see if there is a relationship between the variables. Then if there is any, by using Pearson’s correlation coefficient it is possible to show the direction and the degree of the relationship. The regression line gives you the similar results.
Cevabın yayımlanmış cevapla uyuştu mu?
b = (X'X)^-1 X'y
Cevabın yayımlanmış cevapla uyuştu mu?
If the index number is greater than 100, it indicates an increase compared to the base period value while if it is less than 100, it indicates a decrease compared to the base period value.
Cevabın yayımlanmış cevapla uyuştu mu?
Depending on the number of variables included in the comparison, index numbers can be different. If an index number is used to measure the relative change in just one variable, this indicates a simple index. In the simple index calculation, the ratio of the two variables is converted to a percentage. In other words, index numbers are the percentage of a comparison with a reference value written without the percent symbol. It is important to determine what to write in the reference value field since the simple index calculations can be examined as a fixed based method and chain-based method. In applications, two types of indexes dominate, which are price and quantity indexes.
Cevabın yayımlanmış cevapla uyuştu mu?
A composite index number is constructed to show the changes when there are a number of different items collected together. In some cases, it is desirable to develop an index to compare the aggregation of several substances and the cost of collecting these substances in two different periods. Composite index reflects the average change in the activity of a group of items from the base period to the period under consideration.
Cevabın yayımlanmış cevapla uyuştu mu?
Composite indexes can be examined in two categories, namely weighted and non-weighted, based on the idea that the indexes take into account the importance of the products.
Cevabın yayımlanmış cevapla uyuştu mu?
In order to calculate composite index number, one should be careful about how the items are included in the basket of goods or how the items are organized in in that basket. A basket of goods and services describes a fixed set of commodities for which we may measure changes in price and quantity. When it is assumed that there are no differences between the importance of the commodities in basket, unweighted composite indexes can be used to compare the changes.
Cevabın yayımlanmış cevapla uyuştu mu?
A simple aggregate price index is simply based on accumulating the unit prices of several commodities in different periods. As the name suggests, in this index one looks at the total price of the products in a basket and makes a comparison of this total with another period or with a base period. Therefore, this index will show us the difference of the total price paid to the products in the basket from one period to another period. During the period, the price of some items may be increased while the price of others is decreased, the investigator does not consider the changes in the item price but s/he is interested in the total price change of the products. This index requires the prices of each product in the basket from one period to the other if the price of an item is missing in one period, then the simple aggregate index should not include this item in the final calculations.
Cevabın yayımlanmış cevapla uyuştu mu?
In this method, first a fixed based or chain-based simple indexes are computed for different periods and then one of the averages such as arithmetic mean, geometric mean or median is used to find the averages of the found index values. This index also does not take into account the quantity of each item, but it is still a vast improvement on the simple aggregate index. The simple average of relative method is simpler and easier to apply than the simple aggregative method. The only disadvantage is that it gives equal weight to all items.
Cevabın yayımlanmış cevapla uyuştu mu?
In some situations, it is not possible to look at all the items in a group for which an index calculation is planned, and some items might have more importance or weight than the others among the group members. When all commodities are not of equal importance, a weight of importance to each commodity might be assigned and an index number is computed then it is called weighted index numbers. The use of a weighted index allows the investigator to give greater importance to some of the items in the group. The importance is highlighted by assigning proper weighting to the values of items in proportion to their relative importance. Information other than the change in price over time can be used and can include such factors as the quantity sold or the quantity consumed for each item.
Cevabın yayımlanmış cevapla uyuştu mu?