Conceptually similar to the t-test, ANOVA (analysis of variance) tests whether the means of three or more groups are equal.

The test statistic is the f-ratio, which is essentially:

$$ F=\frac {variance \;between \;groups}{variance \;within \;groups} $$

If the between-groups variation is large compared to the within-groups variation (f-ratio >> 1), it is more likely that the groups have different characteristics.

The test is called the f-test, and the p-value is obtained by referencing the f-ratio in the f-distribution.

Note: A p-value from f-ratio calculator is provided on this webpage. The data analysis add-in in excel provides an easy-to-use facility to conduct the f-test.

H0: μ1 = μ2 = μ3 = μ4 = … = μk

HA: Not all population means are equal

α = 5% (usually)

k: number of groups

Group Means: x̄1,x̄2, x̄3, x̄4 … x̄k

Group Variances: s12, s22, s32, s42 … sk2

Group Sample Sizes: n1, n2, n3, n4 … nk

Total All Samples: nT = n1+n2+n3+n4 … +nk

$$ Average \;All \;Samples: \bar{\bar{x}} = \frac {\sum \bar x × n}{\sum n} $$ $$ F=\frac {variance \;between \;groups}{variance \;within \;groups}=\frac {MS_{between}}{MS_{within}}=\frac{SS_{between}/(k-1)}{SS_{within}/(n_T-k)} $$

Where MS is the mean square, SS is sum of squares, and k ˗ 1 and nT ˗ k are the degrees of freedom.

$$ SS_{between}=\sum_{j=1}^k n_j (\bar x_j - \bar{\bar{x}})^2 $$ $$ SS_{within}=\sum_{j=1}^k \sum_{i=1}^{n_j} (x_{ji} - \bar x_j)^2 = \sum_{j=1}^k(n_j-1)s_j^2 $$
LowMidLow UpperHigh Upper
mean = 10mean = 12mean = 15mean = 19

Exhibit 33.23 Household consumption of wine in litres/year, for 12 respondents, 3 in each income group — low, mid, low upper and high upper.

Example: Household consumption of wine in litres/year, across various income groups is provided in Exhibit 33.23. $$ SS_{between}=\sum_{j=1}^k n_j (\bar x_j - \bar{\bar{x}})^2 $$ $$ \qquad=3×(10-14)^2+ 3×(10-14)^2+3×(10-14)^2 $$ $$ \qquad=138 $$ $$ SS_{within}=\sum_{j=1}^k (n_j-1)s_j^2,\quad s_j^2=\frac{1}{n_j-1} \sum_{i=1}^{n_j}(x_{ji}-\bar x_j)^2 $$ $$ SS_{within}= \sum_{j=1}^k \sum_{i=1}^{n_j} (x_{ji} - \bar x_j)^2 $$ $$ SS_{within}=(8-10)^2+(10-10)^2+(12-10)^2 $$ $$\qquad+(10-12)^2+(12-12)^2+(14-12)^2 $$ $$\qquad+(13-15)^2+(15-15)^2+(17-15)^2 $$ $$\qquad+(17-19)^2+(19-19)^2+(21-19)^2 $$ $$\qquad= 32 $$ $$ F= \frac {138/(4-1)}{32/(12-4)}=11.5 $$

p-value = 0.003 < α=5%. Reject the null hypothesis.

The data suggests that the consumption of wine varies significantly across different household income levels.

Incidentally, regression analysis with dummy variables could also be used instead of ANOVA to determine the size and the direction of the differences in the mean values. For instance, for the previous example: $$Consumption = α + β × Income \, Class, $$ $$\text{Where α is the intercept and coefficient, β, quantifies the effect size.}$$

Previous     Next

Use the Search Bar to find content on MarketingMind.

Marketing Analytics Workshop

Marketing Analytics Workshop

In an analytics-driven business environment, this analytics-centred consumer marketing workshop is tailored to the needs of consumer analysts, marketing researchers, brand managers, category managers and seasoned marketing and retailing professionals.

What they SHOULD TEACH at Business Schools

What they SHOULD TEACH at Business Schools

Is marketing education fluffy too?

Experiential Learning via Simulators | Best Way to Train Marketers

Experiential Learning via Simulators | Best Way to Train Marketers

Marketing simulators impart much needed combat experiences, equipping practitioners with the skills to succeed in the consumer market battleground. They combine theory with practice, linking the classroom with the consumer marketplace.