Introduction Children are highly dependent on their parents because they are their sole providers. Parents' primary responsibility is to provide the basic needs - food, shelter and clothing - of their children. Therefore, parents shape the eating habits of children especially those under the age of 12 years. Generally, children are usually ready to learn how to eat new foods. They also observe the eating behavior of adults around them (Reicks, et al.). However, their eating behaviors evolve as they grow old. Numerous studies have identified factors that influence children eating behavior. They include living condition, access to food, number of caretakers or family members nearby, employment status, age, gender and health condition (Savage, et al.). This paper will estimate the effects that the above factors have on the eating habits of children.

Data

The data for this project was compiled from various internet sources. All the statistical analysis was carried out using Microsoft Excel statistical software. Descriptive statistics indicates the mean, median, standard deviation, maximum, and minimum values of each variable. The correlation coefficient, r, measures the strength of the linear relationship between any two variables. Regression analysis predicts the influence of one or more explanatory (independent) variables on the dependent variable

Descriptive Statistics

Descriptive statistics were used to describe the variables used in this project. The results are displayed in Table A1 (Appendix A). Eating behavior scores ranges from 41 to 100 (M = 71.07, SD = 17.47). A higher eating behavior score reflects a healthy eating behavior. The average age of the subjects is 6.39 years. Most of the subjects live in a developed area (60.20%) and their parents are employed (65.31%). Also, most of the households are made up of both parents. Almost half of the subjects were male (53.06%). 54.08% of the subjects do not use electronics at mealtimes. Approximately half of the subjects (56.12%) confirmed that the availability of food was limited.

Correlation

Correlation results are displayed in Table B1 (Appendix B). It is clear that the independent variables are not correlated.

Regressions and Interpretations

Regression analysis was performed to predict eating habits among children. Four different regression equations were estimated. Each of the equations is described below.

Regression Equation 1

Eating Behavior = ?0 + ?1living location + ?2Access to food + ?3Age + ?4Gender + ?5Electronic use

Equation (1) is the base regression model for estimating eating behavior in children. It shows the linear relationship between eating behavior and the key explanatory variables (living location, access to food, age, gender, and electronic use). The Excel results of estimating this equation are displayed in Table C1 (Appendix C). The estimated equation is follows:

Eatingbehavior = 75.598 + 2.253 livloc - 7.643Foodacc - 0.572 Age

(t) (15.11) (0.62) (- 2.20) (- 1.17)

- 4.268Gender + 7.375Elec use R2 = 0.1133

(- 1.23) (2.10)

All variables are insignificant at 5 percent level expect except access to food (p-value = 0.03) and electronic use (p-value...

I further perform t-test to determine whether access to food has a negative effect on eating behavior and whether the use of electronics during mealtime affects eating behavior. First, the null and alternative hypothesis of access to food is
H0: ?2 = 0

HA: ?2 0

The test statistic for access to food is – 2.20. At 5 percent significance level, the critical value of t-distribution with N – 5 = 93 degrees of freedom is, t (0.95, 93) = 0.063. Since the calculated value falls in the rejection region, I reject the null hypothesis that ?2 = 0 and conclude that the coefficient of access to food is nonzero (Hill, et al. 109).

Secondly, the null and alternative hypothesis of electronic use is

H0: ?5 = 0

HA: ?5 0

Since t = 2.10 is greater than 0.063, I reject the null hypothesis that ?5 = 0 and conclude that the coefficient of electronic use is statistically significant. This test confirms that if children do not use electronics during mealtimes, their eating habits improve.

R- Squared is 0.1133. It means that the regression model explains 11.33% of the variation in eating behavior.

Regression Equation 2

Eating Behavior = ?0 + ?1living location + ?2Access to food + ?3Age + ?4Gender + ?5Electronic use + ?6Household

In equation (2), the first proxy, the household is added to the model. The Excel results of estimating this equation are displayed in Table D1 (Appendix D). The estimated regression equation is as follows:

Eatingbehavior = 75.366 + 2.225 livloc - 7.674Foodacc - 0.572 Age

(t) (14.1) (0.61) (-2.20) (- 1.16)

- 4.282Gender + 7.414Elecuse + 0.470Household R2 = 0.1134

(- 1.22) (- 2.09) (0.14)

In this model, household is statistically insignificant (p-value = 0.892791184). Therefore, household type (single parent or both parents) does not influence the eating behavior of children. The value of R – Squared remained unchanged at 0.1134. It means that the addition of family structure did not improve the fit of the model. The coefficients of the variables changed slightly compared to the coefficients of the base model. The estimate of electronic use during mealtimes increased from 7.375 to 7.414. However, the coefficient of age remained unchanged at – 0.572.

Regression Equation 3

Eating Behavior = ?0 + ?1living location + ?2Access to food + ?3Age + ?4Gender + ?5Electronic use + ?6Employment status

In equation (3), the second proxy, employment status is included in the base model. The Excel results of estimating this equation are displayed in Table E1 (Appendix E). The estimated equation is as follows:

Eatingbehavior = 76.937 + 2.386 livloc - 7.630Foodacc - 0.600 Age

(t) (14.09) (0.65) (- 2.19) (- 1.22)

3.833Gender + 7.449Elec use – 2.307Empstatus R2 = 0.1170

(- 1.08) (2.11) (- 0.62)

The effects of the second proxy (employment status) are almost similar to the effects of the first proxy (household). The coefficient of employment status is insignificant at 5 percent level (p-value is 0.53510793). The value of R- Squared changed slightly. It means that the inclusion of the second proxy...