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However, there is no consensus on what constitutes an adequate sample size for longitudinal research in public health. There are several studies (Lambek, [@B17]; Laaksonen et al., [@B16]; Whaley, [@B32]; Hosmer et al., [@B13]) that have examined the effects of sample size on the results of logistic regression analysis. However, those studies do not contain any concrete and reasonable guidelines on the sample size for logistic regression. For example, previously, almost all studies of linear regression analysis in health psychology had dealt with the issue of sample size by only examining the required sample size in terms of response rate (Wainer et al., [@B30]; Peyrot et al., [@B23]). When we examine those studies that deal with logistic regression, we notice that all of them simply indicate that “logistic regression requires more data than logistic analysis” (Hosmer et al., [@B13]). Therefore, our aim in this paper is to obtain new guidelines on sample size for logistic regression. We obtained the guidelines using a simulation procedure with a true model. The success of the guidelines is evaluated by comparing two approaches that use the same sample size and that apply the guidelines to the real data (a comparison between the proposed guidelines and previously suggested approaches).

Other than the above, we also examined the effects of sample size on the accuracy of parameter estimation by using Monte Carlo simulation, and presented useful tables on the relationship between sample size and the accuracy of parameter estimation. These results may contribute to exploring the effects of sample size on the results of the linear and logistic regression analyses, since when researchers design longitudinal studies in health psychology, they often compare the results of the longitudinal data analysis with the results of previous studies that had used cross-sectional data. There are several previous studies (e.g., Hosmer et al., [@B13]; Moon et al., [@B21]) that dealt with the effects of sample size on the results of longitudinal analysis. However, to the best of our knowledge, none of them examined those effects by using samples with different sizes. Our study is the first to report the effects of sample size on the results of
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MySQL Exception on connect in Java

Why am I getting this exception? The database is running on the same machine.
java.sql.SQLException: No suitable driver found for jdbc:mysql://localhost/test

This is the code I am using:
public static void main(String[] args) {
Connection con = null;
try {

Class.forName(“com.mysql.jdbc.Driver”);
con = DriverManager.getConnection(“jdbc:mysql://localhost/test”, “root”, “password”);
Statement st = con.createStatement();

ResultSet rs = st.executeQuery(“select * from users”);

System.out.println(rs.getString(2) + ” : ” + rs.getString(3));

} catch (Exception ex) {

ex.printStackTrace();

}

try
Class.forName(“com.mysql.jdbc.Driver”);
con = DriverManager.getConnection(“jdbc:mysql://localhost/test”, “root”, “password”);

By the way, why don’t you use Commons DBCP for connecting to MySQL databases? It

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this.methods.Add.Name = “Add”;
this.methods.Remove.Name = “Remove”;
this.methods.Equals.Name = “Equals”;
this.methods.NotEquals.Name = “NotEquals”;
this.methods.GetHashCode.Name = “GetHashCode”;
this.methods.GetType.Name = “GetType”;
this.methods.ToString.Name = “ToString”;

}
And adding this in the interface

protected override TypeConverter[] ConvertCore(MemberInfo [] array, Type [ ] types)
{
// required
TypeConverter tc = null;
if (array!= null)
{

return new TypeConverter[]
{

this.methods.GetType.MakeGenericType(types[0]),
this.methods.GetHashCode.MakeGenericType(types[0]),
this.methods.Equ

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