**Excel Assignment Help**

Students seeking excel assignment help can refer to the below sample report and get insight of our quality. Some important aspects of statistical analysis of data are covered here like descriptive statistics, correlation co-efficient, and scatter plots. Results were interpreted and conclusions were drawn on the objective findings from the stats homework help report.

The purpose of the present study is to analyze the factors that influence the foster care in U.S., more exactly the effect of welfare benefits on Excel assignment help children entering foster using data from 2010-2014 for all 50 states and the District of Columbia (grouped in four regions). The findings have shown that all the variables Excel homework help affects in a small proportion the foster care. Moreover, there are some pay for Excel homework discrepancies of foster care among the four regions of U.S.

**Part I: Descriptive Statistics and Relationships**

The motivation of the present study is that foster care is one of the areas that welfare benefits can possibly have an effect on. In this context,analyzing the factors that can influence the proportion of children entering foster care each year per 1,000 Excel assignment help children born in U.S. is necessary.

In this part we created a table of descriptive statistics. As it can be observed in the table below, all the nine variables have 255 valid cases, Excel homework help meaning that there are not missing observations from the sample.

**Table 1: Descriptive Statistics**

The average number of children entering foster care each year per 1,000 children born (*Foster*) is 74.73, with a minimum value of 25.29 and a Excel assignment solution maximum value of 188.41. The standard deviation is 29.37.

In the considering data sample, the mean value of average earned income tax credit payment in 2014 (the variable *EITC*) is 2308.30, with a minimum value of 1841.78and a maximum value of 2875. The standard deviation is 210.73.

The average number of children receiving temporary assistance for needy families per 1000 children (*TANF*) is 38.19, with a statistics using Excel assignment help minimum value of 3.70and a maximum value of 166.25. The standard deviation is 26.97.

The mean value of the portion of TANF funds going directly towards childcare per $1000 (*TRANSF*) is 161.71, with a minimum of 0and a maximum value of 678. The standard deviation is 130.29.

The poverty rate (*Poverty*) in the U.S. has an average value of 14.11, with a minimum of 7.2 and a maximum of 27.6. The standard deviation is 3.45.

In the case of control variables of regions (since they are dummy variables – binary variables which takes the value 1, if the condition is accomplished and 0 otherwise), the maximum value is 1 and the minimum is 0. The standard deviation is here quite small.

It can also be observed that there are some values for Kurtosis below 0 (negative numbers), which can indicate a distribution that is Excel problems with answers relatively flat (too many cases in the extremes), but this risk is reduced because in our case we have a large sample over 200 observations.

Here, we have construct a matrix of correlation coefficients.The strength of the correlation coefficient can be interpreted as the absolute Excel assignment solutionvalue for *r*, which can be: very week (0.00-0.19), weak (0.20-0.39), moderate (0.40-0.59), strong (0.60-0.79), and very strong (0.80-1.0).

**Table 2:Matrix of Correlation Coefficients**

To analyze the associations between the Excel homework solution considered variables,we must look at the Correlation Matrix presented in the table below. Here we must interpret the Pearson Correlation for each variable.

- First, each variable perfectly correlates positively with itself (1 defines a perfect positive correlation).
- There is a negative week correlation between
*Foster*and*EITC*(*r*=-0.2539), meaning that a change in*EITC*will determine a data analysis using make my Excel assignment change in the opposite direction in*Foster*(when*EITC*increases, for example,*Foster*decreases); - There is a negative very week correlation between
*Foster*and*TANF*(*r*=-0.0301). - There is a negative very week correlation between
*Foster*and*TRANSF*(*r*=-0.044). - There is a positive very week correlation between
*Foster*and*Poverty*(*r*=0.0302), meaning that a change in*Poverty*will determine a change in the same direction in*Foster*(when*Poverty*increases, for example,*Foster*increases too); - There is a negative very week correlation between
*Foster*and Region*NEAST*(*r*=-0.0732). - There is a positive very week correlation between
*Foster*and Region*MWEST*(*r*=0.1731). - There is a negative very week correlation between
*Foster*and Region*SOUTH*(*r*=-0.1964). - There is a positive very week correlation between
*Foster*and Region*WEST*(*r*=0.1079).

If *Foster* is considered the dependent (predictive) variable and the rest of eight variables are independent, it can be noted that these explanatory variables are not very highly correlated with each other (the correlation coefficient between thestatistics using Excel homework help independent variables is far from beingvery close to the perfect correlation of 1). It means that multi collinearity would not be a problem in the model.

Here, we have construct the scatter plots of selected variables.

**Figure 1:Scatter plot between FOSTER and EITC**

We have found from the previous section of the paper that there is a negative week correlation between *Foster* and *EITC*, relation which is Excel homework for money also shown by the scatter plot. It can be observed that the linear trend line is negative (the red line from the graph), and the cloud of points is dispersed rather than concentrated around this line, with some outliers. The linear relationship help with Excel homework between *Foster* and *EITC* is not a strong one, but week.

**Figure 2:Scatter plot between FOSTER and TANF**

From the correlation coefficients we have found that there is a negative very week correlation between *Foster* and *TANF*, relation which is also shown by the scatter plot. It can be observed that the linear trend line is negative (the red line from the graph slowly decreases).The cloud of points is even more dispersed around this line compared with the previous situation.Some outliers are also presented. The linear relationship between *Foster* and *EITC* is a very week one.

**Figure 3:Scatter plot between FOSTER and TRANSF**

From the previous part of the paper we have found that there is a negative very week correlation between *Foster* and *TRANSF*, relation which is also shown by the scatter plot. It can be observed that the linear trend line is negative (the red line from the graph decreases). The cloud of points is dispersed around this line, with a concentrationhelp with Excel assignment in the left part of the graph and several outliers. The linear relationship between *Foster* and *TRANSF* is a very week one.

**Figure 4:Scatter plot between FOSTER and POVERTY**

From the correlation coefficients we have found that there is a positive very week correlation between *Foster* and *Poverty*, relation which is alsohelp with Excel homework shown by the scatter plot. It can be observed that the linear trend line is positive (the red line from the graph slowly increases). The cloud of points is dispersed around this line. Some outliers are also presented. The linear relationship between *Foster* and *EITC* is a very week one.** **

**Part II: Accounting for foster care by regions**

In this part, we will describe the experience do my Excel assignment of foster care by four regions(NEAST, MIDWEST, SOUTH and WEST)characteristics. In order to do this, we must analyze the summary statistics (the mean), which are shown in the table above.

It can be observed that the minimum value of *Foster* is in the *SOUTH* region and the maximum value is in *MWEST*. The minimum value of *EITC* is in the *NEAST* region and the maximum value is in *SOUTH*. The minimum value of *TANF* is in the *MWEST* region help with Excel assignmentand the maximum value is in *NEAST*. The *TRANSF* reach their minimum value in *WEST* and their maximum value in *MWEST*. The *NEAST* region is the less affected by *Poverty* and the *SOUTH* has the highest rate.** **

**Part III: Conclusion and Policy recommendation to HHS**

The ** purpose** of this study was to analyze the effects of nine variables on foster care. The

**have shown that children entering foster care each year per 1,000 children born(**

*key findings of the study**FOSTER*) are negative and very week data analysis using Excel assignment help influenced by the number of children receiving temporary assistance for needy families per 1000 children (

*TANF*) and by the portion of

*TANF*funds going directly towards childcare per $1000 (

*TRANSF*). The Poverty rate (

*POVERTY*) positively affects the

*FOSTER*variable, but in aExcel assignment help very small proportion (the correlation between the variables is very week). Only the average earned income tax credit payment in 2014(

*EITC*) seems to influence a more powerful (negative) influence on

*FOSTER*(compared with the other considered variables), but the overall influence is still week.

When considering the four regions of U.S., all of them Excel project help influence in a very week proportion the *FOSTER*. However, *NEAST* and *SOUTH* determine pay for Excel assignment a negative correlation, and *MWEST* and *WEST* positive one.

The *FOSTER* is more pronounced in the *MWEST*, a region in which the number of children receiving temporary assistance for needy help with Excel assignmentfamilies per 1000 children (*TANF*) is the lowest and the portion of TANF funds going directly towards childcare per $1000 (*TRANSF*) is the highest.

*FOSTER* has the lower value in *SOUTH*, even if this region is the most affected by *Poverty*. Since this conclusion seems somehow at odds with the economic logicthis topic can be for sure ** an extension for future research**.It would be more likely that in the poor regions, because people have little money to support their children, the number of children entering foster care each year per 1,000 children born to be higher compared with the wealthiest regions. However, this topic can be analyzed in a further research, aExcel questions with answers possible explanation to this fact being the birth and mortality rate. However, a crucial factor to Excel assignment help this situation could also be the fact that this region (probably because it is perceived as the poorest from U.S.) received the highest amount of average earned income Excel homework help tax credit payment in 2014 (

*EITC*), but the statistics using Excel assignment help subject needs further investigations, since the region (

*NEAST*) with the lowest level of

*EITC*did not have the highest level of

*FOSTER*.

Given the overall findings, as a Excel project help ** policy recommendation **to Department of Health and Human Services (HHS)we consider that since EITC was found to exercise the most powerful influence of the reduction statistics using Excel assignment help of FOSTER, the governmentExcel assignment help al bodies should focus on means to increase the average earned income tax credit payment.